Full-automatic subway smooth running control method and device, terminal and storage medium

Abstract

The application provides a full-automatic subway smooth running control method, device, terminal and storage medium, wherein the method comprises the following steps: testing the manual driving level of a subway driver by using an equivalent model to obtain a test result; when the equivalent model is stable and does not fall in the whole line operation of the subway, the test result is qualified; collecting the driving data of the subway driver with the qualified test result to obtain sample data; analyzing the sample data to obtain a traction and braking control curve graph; optimizing the operation logic of the subway according to the traction and braking control curve graph, and controlling the subway to perform whole line actual measurement verification to determine an optimal control parameter scheme; and controlling the automatic operation of the subway by using the optimal control parameter scheme. The full-automatic subway smooth running control method, device, terminal and storage medium have great improvement on the smoothness of full-automatic subway unmanned driving, and provide passengers with a smooth and inappreciable riding experience.

Description

Technical Field

[0001] This invention belongs to the field of rail transit technology, and in particular relates to a fully automatic subway smooth operation control method, device, terminal and storage medium. Background Technology

[0002] Subway signaling systems typically employ PID controllers, which control the deviation between the setpoint and the output value using integral, derivative, and proportional methods. This approach results in excessive acceleration and deceleration switching when controlling speed, which is detrimental to the fully automatic and smooth operation of subway trains.

[0003] The current traction control model assumes that the acceleration of the subway train from a standstill at zero speed on the platform to starting again is 1.5 m / s². 2 The initial traction level at the platform is 40%, and the traction level within the platform area is limited to 43%. After the rear of the metro train clears the platform, it will then move at 1.5 m / s. 2 The acceleration continues unabated, no longer limiting the superposition of acceleration levels. This results in a steep starting slope and a secondary acceleration step after leaving the station, leading to a poor passenger experience. Before the subway train enters the station, the system should control the train to change speed smoothly. When the train approaches the station's stopping point, the traction command should be gradually reduced to zero in advance, preparing for light braking or inertial deceleration to maintain a stable speed change. The current system, while controlling the train's deceleration according to normal control instructions, exhibits a significant instantaneous steep deceleration just before entering the station. Simultaneously, the deceleration slope changes noticeably just before stopping, causing a jerky feeling during deceleration and reducing passenger comfort. Summary of the Invention

[0004] In view of this, the present invention aims to propose a fully automatic subway smooth operation control method, device, terminal and storage medium to solve the problem that the existing fully automatic subway control methods result in poor subway smooth operation and affect the comfort of subway passengers.

[0005] To achieve the above objectives, the technical solution of the present invention is implemented as follows: In a first aspect, embodiments of the present invention provide a fully automated subway smooth operation control method, including: The driving skills of subway drivers were tested using an equivalent model, and the test results were obtained. The test result was considered qualified if the equivalent model remained stable throughout the operation of the entire subway line. The driving data of subway drivers whose test results were qualified were collected to obtain sample data; The traction and braking control curves were obtained by analyzing the sample data. The operation logic of the metro is optimized based on the traction and braking control curves, and the metro is tested and verified along the entire line to determine the optimal control parameter scheme. The optimal train control parameter scheme is used to control the automatic operation of the subway.

[0006] Furthermore, the test of the subway driver's manual driving skills using an equivalent model yields test results, including: The equivalent model was used to test the manual driving skills of subway drivers; The test result is qualified when the equivalent model with a diameter of 3cm and a height of 20cm does not fall. The test result is good when the equivalent model with a diameter of 4cm and a height of 30cm does not fall. The test result is excellent when the equivalent model with a diameter of 3cm and a height of 30cm does not fall over.

[0007] Furthermore, the driving data of subway drivers whose test results are qualified is collected to obtain sample data, including: An analysis was conducted on the lever level control and speed control of subway drivers whose test results were qualified. The goal is to acquire an excellent subway driver whose control accuracy and speed at the handle level are within 5%, and to enable the excellent subway driver to perform multiple full-line subway operations. Driving data from multiple subway line operations was collected to obtain sample data.

[0008] Furthermore, the step of analyzing the sample data to obtain traction and braking control curves includes: The sample data is summarized and analyzed to obtain vehicle control curves with smooth traction and braking. The vehicle control curves were analyzed to determine the level change pattern during acceleration and the timing of electro-pneumatic braking transition during deceleration, yielding the analysis results. Based on the analysis results, vehicle control curves for different starting accelerations and different air brake pre-application pressure ratios were plotted.

[0009] Furthermore, the optimization of the metro's operating logic based on the traction and braking control curves, and the control of the metro through full-line field testing to determine the optimal control parameter scheme, includes: The train control curves with different starting accelerations and different air brake pre-application pressure ratios were selected, and the entire metro line was tested and verified using each of the train control curves to obtain the full-line test and verification results. Obtain the full-line measured verification results, and select the train control curve corresponding to the optimal full-line measured verification results to obtain the optimal train control parameter scheme for the entire metro line.

[0010] Furthermore, the optimal vehicle control parameter scheme includes: The acceleration from a standstill at zero speed on the platform to starting again is 1 m / s². 2 ; The traction level limit within the platform is 40%; During the secondary acceleration after leaving the platform, the traction level increases by 0.1% every 200ms; The air brake preload ratio is 25% when entering the platform.

[0011] Secondly, embodiments of the present invention also provide a fully automatic subway smooth operation control device, comprising: The testing module is used to test the driving skills of subway drivers using an equivalent model and obtain test results. The test result is considered qualified if the equivalent model remains stable throughout the entire subway line operation. The data acquisition module is used to collect driving data from subway drivers whose test results are qualified, and obtain sample data. The analysis module is used to analyze the sample data to obtain traction and braking control curves. The optimization module is used to optimize the operation logic of the metro based on the traction and braking control curves, and to control the metro to conduct full-line test verification to determine the optimal control parameter scheme. The control module is used to control the automatic operation of the subway using the optimal train control parameter scheme.

[0012] Thirdly, embodiments of the present invention also provide a terminal, including: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the fully automatic subway smooth operation control method described above.

[0013] Fourthly, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the fully automatic subway smooth operation control method described above.

[0014] Compared with existing technologies, the fully automatic subway smooth operation control method, device, terminal and storage medium described in this invention have the following advantages: The fully automated metro smooth operation control method, device, terminal, and storage medium described in this invention significantly improve the smoothness of fully automated driverless metro systems, providing passengers with a smooth and imperceptible riding experience. Furthermore, it reduces the pure traction energy consumption of metro trains, achieving energy conservation and emission reduction. Simultaneously, the optimization of traction and braking control logic helps reduce current, mitigates significant electrical wear between the pantograph and catenary, eases the pantograph-catenary relationship, and continuously reduces the wear rate of the carbon sliding plate and contact wire. Attached Figure Description

[0015] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating the fully automatic subway smooth operation control method described in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the fully automatic subway smooth operation control device described in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of the fully automatic subway smooth operation control terminal provided in Embodiment 3 of the present invention. Detailed Implementation

[0016] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0017] Example 1 Figure 1 This is a flowchart illustrating the fully automated subway smooth operation control method described in Embodiment 1 of the present invention. See also... Figure 1 This method specifically includes the following steps: Step 101: Use an equivalent model to test the manual driving skills of subway drivers and obtain the test results; if the equivalent model remains stable throughout the entire subway line operation, the test result is considered qualified.

[0018] Specifically, all drivers can undergo manual driving practice using the CM (computer interface) system. The drivers' control of the lever position and speed can be analyzed, and outstanding drivers who can control the lever position and speed within 5% can be selected to run multiple subway lines.

[0019] While there are objective evaluation indicators for subway train punctuality, stopping accuracy, and energy consumption, operational comfort primarily refers to passenger experience during the journey, making it a more subjective evaluation indicator. To effectively conduct research on comfort improvement, this project investigated the criteria and standards for comfort judgment and conducted experiments using methods such as keeping a water cup from spilling, keeping a wooden stick from falling over, and keeping a coin from tipping over. Ultimately, simple, intuitive, and effective wooden sticks of varying heights and thicknesses (from easiest to hardest: 3cm diameter / 20cm height, 4cm diameter / 30cm height, and 3cm diameter / 30cm height) were selected. These sticks were placed on the subway train floor, and the success rate of keeping the sticks upright during train operation was used as the standard for judging subway train operational comfort.

[0020] In this embodiment, an equivalent model is used to test the manual driving skills of subway drivers, and the test results are obtained. The specific steps include the following: The driving skills of subway drivers were tested using an equivalent model. The test result was considered qualified when the equivalent model with a diameter of 3cm and a height of 20cm did not fall over; the test result was considered good when the equivalent model with a diameter of 4cm and a height of 30cm did not fall over; and the test result was considered excellent when the equivalent model with a diameter of 3cm and a height of 30cm did not fall over.

[0021] In practical application, actual tests showed that, before optimization, the comfort performance of the subway train in driverless mode was such that all three types of wooden sticks would fall over during operation conditions such as starting, clearing the platform, accelerating, decelerating, and stopping at the station, with a success rate of 0%. However, under the manual driving of an excellent driver, the tested wooden sticks remained stable throughout the entire operation, with a success rate of 100%. Therefore, based on the test results, the comfort improvement target was set to reach the level of an excellent driver, that is, when the subway train is running automatically, the tested wooden sticks should remain stable throughout the entire operation: 3cm diameter / 20cm height (qualified), 4cm diameter / 30cm height (good), and 3cm diameter / 30cm height (excellent).

[0022] Step 102: Collect driving data from subway drivers whose test results are qualified to obtain sample data.

[0023] In this embodiment, the driving data of subway drivers whose test results are qualified are collected to obtain sample data, specifically including the following steps: The control performance of the handle level and speed of subway drivers who passed the test were analyzed; excellent subway drivers with control accuracy of handle level and speed control within 5% were selected, and these excellent subway drivers were allowed to run multiple subway full-line operations; driving data from these multiple subway full-line operations were collected to obtain sample data.

[0024] Based on the above test results, a special train driver training class can be formed by selecting drivers with excellent manual driving skills, and a detailed standardized driving guide manual can be compiled. The manual strictly stipulates the level control of the driver's controller lever, controls the control accuracy and speed of the lever level within 5%, accurately controls the speed, drives at the prescribed speed throughout the journey, clarifies the timing and output value of the driver's controller, and formulates training and assessment standards for multiple modes.

[0025] In practical application, after manual driving training with standardized manual driving guidelines, approximately 6,000 kilometers of driving data were sampled from all excellent drivers to ensure that the most stable and timely driving data was collected at every point in every station along the entire line. The big data was then aggregated to form an optimal driving curve for analysis and modeling.

[0026] Through analysis, the characteristics of human driving data can be summarized as follows: 1) Traction phase: During start-up and secondary acceleration, the speed increases smoothly. The subway train is controlled to gradually increase the traction force, so that the acceleration gradually increases and is kept within an appropriate range. At the same time, the level changes slowly when adjusting the level.

[0027] 2) Braking phase: During deceleration, the braking force can be adjusted at any time according to the actual situation such as the different positions of the subway train, the remaining distance, and the braking speed. For example, under high-speed braking conditions, the braking level can be increased from small to large in advance, and then the braking level can be decreased from large to small in appropriate positions. Finally, the subway train can be controlled to enter the platform with small braking or coasting (in accordance with the platform speed limit). When approaching the stopping point, coasting is the main method while applying small braking force to stop the subway train. The advantage of doing this is that it can slow down the electric braking withdrawal rate and reduce the impact of air braking.

[0028] The challenge of system-based train control lies in the fact that the actual conditions at each location along the entire line, for each subway train, and for each driving session are not entirely consistent, making system control unlike manual driving which allows for real-time adjustments. Therefore, it is necessary to use manual driving data as a basis, repeatedly adjust the system control logic, and select a control scheme suitable for the entire line through full-line testing and verification.

[0029] Step 103: Analyze the sample data to obtain traction and braking control curves.

[0030] Specifically, the sample data can be used to analyze the optimal traction and braking control curves, and the logic of vehicle starting, acceleration, deceleration and stopping can be optimized based on the curves. The subway can then be controlled for full-line real-world testing and verification to determine the optimal vehicle control scheme that ensures the total turnaround time remains unchanged while significantly improving comfort.

[0031] In this embodiment, the traction and braking control curves are obtained by analyzing the sample data, specifically including the following steps: First, the sample data is summarized and analyzed to obtain vehicle control curves showing smooth traction and braking. Second, the vehicle control curves are analyzed to determine the level changes during acceleration and the timing of electro-pneumatic braking transition during deceleration, yielding analytical results. Finally, based on these results, vehicle control curves for different starting accelerations and different air brake pre-application pressure ratios are plotted.

[0032] In practical applications, based on the analysis results, it is necessary to further derive the variation patterns of the smooth curve, traction acceleration, and braking deceleration, establish a model for the signal control parameters, and draw smooth curves showing the variation patterns under different traction accelerations and different pre-applied pressure ratios of the air brake, i.e., control curves for different starting accelerations and different air brake pre-applied pressure ratios.

[0033] Specifically, the challenge in optimizing traction control lies in simultaneously improving passenger comfort while ensuring the efficiency, punctuality, and operational capability of subway trains under conditions such as malfunctions or gradients. To address this, the optimization of train control logic, including starting, secondary traction, acceleration limits, and phase stacking, was achieved. This involved streamlining the number of traction and braking operations and optimizing the timing of traction and braking control. After multiple rounds of model parameter adjustments, optimizations, and testing, a final optimized traction control model scheme for subway trains was developed, ensuring punctuality, operational capability, and passenger comfort.

[0034] Meanwhile, based on the above analysis of braking control strategies and driver manual driving data, the braking control logic optimization scheme mainly includes the following two aspects: 1) Parking optimization solution: After repeated testing and adjustments, it was found that the core issue causing the noticeable stopping jerk was that during the electro-pneumatic braking transition, the air brake intervened earlier and applied a larger braking force. This resulted in the air brake being applied again before the electric brake had fully disengaged, causing the noticeable stopping jerk. Therefore, multiple rounds of control parameter adjustments and verifications were required. Under the premise of ensuring the braking capacity of the subway train and accurate calibration, the optimized control model for braking conditions focused on optimizing the coordination logic of the braking system with the electro-pneumatic brake.

[0035] 2) Deceleration optimization scheme: During the deceleration process before the subway train arrives at the platform, it first applies light braking or uses coasting to maintain a smooth change in speed, ensuring that the deceleration of the subway train does not fluctuate significantly. At the same time, it decelerates at a certain slope to avoid sudden changes in deceleration.

[0036] Step 104: Optimize the subway's operation logic based on the traction and braking control curves, and conduct full-line actual testing and verification of the subway to determine the optimal control parameter scheme.

[0037] Specifically, by using the data analysis from the test bench and the results of actual measurements on the main line, the comfort level and total turnaround time of the metro train can be tested. After obtaining the actual measurement verification results for the entire line, the optimal train control parameter scheme for the entire metro line can be determined to control the automatic operation of the metro.

[0038] In this embodiment, the subway's operating logic is optimized based on the traction and braking control curves, and the subway is controlled for full-line real-world testing and verification to determine the optimal control parameter scheme. Specifically, the following steps are included: First, train control curves with different starting accelerations and different air brake pre-application pressure ratios are selected, and the entire metro line is tested and verified using each of these train control curves to obtain the full-line test verification results. Then, the full-line test verification results are obtained, and the train control curve corresponding to the optimal full-line test verification result is selected to obtain the optimal train control parameter scheme for the entire metro line.

[0039] In practical applications, the train control curves with different starting accelerations and different air brake pre-application pressure ratios can be upgraded across all subway trains. A week-long field test can be conducted to verify the improvement in operating comfort and traction energy consumption of the subway trains. Then, the train control curve with the best improvement in comfort and traction energy consumption can be selected as the optimal train control parameter scheme for the entire subway line.

[0040] Specifically, during the main line operating hours, the operation of 30 trains across 15 subway trains can be investigated to cover the entire route. Under the premise that the punctuality rate meets the requirements, the success rate of testing the three types of wooden sticks without falling over can be increased from 0% to 100%, thus determining the established target for improving comfort.

[0041] Step 105: Use the optimal train control parameter scheme to control the automatic operation of the subway.

[0042] In this embodiment, the optimal vehicle control parameter scheme includes: The acceleration from a standstill at zero speed on the platform to starting again is 1 m / s². 2 The traction level within the platform is limited to 40%; during secondary acceleration after leaving the platform, the traction level increases by 0.1% every 200ms; when entering the platform, the air brake pre-pressure application ratio is 25% until the subway comes to a stop at zero speed on the platform.

[0043] Specifically, under the premise of ensuring the braking capacity of the subway train and accurate calibration, the control model optimization scheme under braking conditions is to optimize the coordination logic of the braking system with electro-pneumatic braking, adjust the air brake pre-pressure application ratio from 75% to 25%, smooth the air brake application acceleration during electro-pneumatic conversion, improve the smoothness of the subway train braking process, and weaken the deceleration jerking sensation.

[0044] In the actual application process, the optimal train control parameter scheme can be used to control the subway to perform actual measurement operation. Compare the vehicle-kilometer power consumption in one week after optimization and upgrading with the vehicle-kilometer power consumption in one week before optimization and upgrading. After analysis according to the optimization and upgrading of this scheme, it can be found that the maximum current of the subway train after optimization is reduced, and the traction energy consumption is also reduced. Therefore, optimizing subway control is also conducive to reducing current, and can effectively reduce the traction energy consumption of subway trains and pantograph-catenary wear.

[0045] In this embodiment, the equivalent model is used to test the manual driving level of subway drivers to obtain test results. Wherein, when the equivalent model runs stably without tipping over during the whole line operation of the subway, the test result is qualified. Collect the driving data of subway drivers with qualified test results to obtain sample data. Analyze and obtain traction and braking train control curves using the sample data. Optimize the operation logic of the subway according to the traction and braking train control curves, and control the subway to carry out full-line actual measurement verification to determine the optimal train control parameter scheme. The optimal train control parameter scheme is used to control the automatic operation of the subway. It can greatly improve the stability of fully automatic driverless subway and provide passengers with a smooth and imperceptible riding experience.

[0046] Embodiment 2 Figure 2 is a schematic structural diagram of the fully automatic subway smooth operation control device according to Embodiment 2 of the present invention. Figure 2 shows a block diagram of an exemplary system suitable for implementing the embodiments of the present invention. Figure 2 The system shown is only an example, and should not impose any restrictions on the functions and scope of use of the embodiments of the present invention, see Figure 2 , this device specifically includes: The test module 201 is configured to test the manual driving level of subway drivers by using an equivalent model to obtain a test result. Wherein, when the equivalent model runs stably without tipping over during the whole line operation of the subway, the test result is qualified.

[0047] The collection module 202 is configured to collect driving data of subway drivers with qualified test results to obtain sample data.

[0048] The analysis module 203 is configured to obtain traction and braking train control curves through analysis using the sample data.

[0049] The optimization module 204 is configured to optimize the operation logic of the subway according to the traction and braking train control curves, and control the subway to perform full-line actual measurement verification to determine the optimal train control parameter scheme.

[0050] The control module 205 is configured to control the automatic operation of the subway by using the optimal train control parameter scheme.

[0051] The fully automatic subway smooth operation control device described in this embodiment obtains a test result by testing the manual driving level of subway drivers using an equivalent model; wherein, when the equivalent model operates stably without tipping over during the entire line operation of the subway, the test result is qualified; collecting the driving data of subway drivers with the qualified test result to obtain sample data; obtaining traction and braking vehicle control curves by analyzing the sample data; optimizing the operation logic of the subway according to the traction and braking vehicle control curves, and controlling the subway to conduct full-line actual measurement verification to determine the optimal vehicle control parameter scheme; using the optimal vehicle control parameter scheme to control the automatic operation of the subway. It can improve the smoothness and comfort of fully automatic subway operation, and is beneficial to improving the riding experience of subway passengers.

[0052] On the basis of the above embodiments, the test module includes: A level test unit, configured to test the manual driving level of subway drivers using an equivalent model; when the equivalent model with a diameter of 3 cm and a height of 20 cm does not tip over, the test result is qualified; when the equivalent model with a diameter of 4 cm and a height of 30 cm does not tip over, the test result is good; when the equivalent model with a diameter of 3 cm and a height of 30 cm does not tip over, the test result is excellent.

[0053] On the basis of the above embodiments, the acquisition module includes: A situation analysis unit, configured to analyze the handle position control and speed control situation of subway drivers with the qualified test result.

[0054] An acquisition unit, configured to acquire excellent subway drivers whose control accuracy of handle position and speed control are within 5%, and make the excellent subway drivers perform multiple full-line operations of the subway.

[0055] A sample collection unit, configured to collect the driving data of the multiple full-line operations of the subway to obtain sample data.

[0056] On the basis of the above embodiments, the analysis module includes: An analysis unit, configured to summarize and analyze the sample data to obtain vehicle control curves with gentle traction and braking.

[0057] A sorting unit, configured to sort the vehicle control curves, analyze the position change rule during starting acceleration and the electro-pneumatic braking conversion timing during braking deceleration, so as to obtain an analysis result.

[0058] A drawing unit, configured to draw vehicle control curves with different starting accelerations and different pre-applied air brake pressure ratios according to the analysis result On the basis of the above embodiments, the optimization module includes: The verification unit is used to select the control curves of different starting accelerations and different air brake pre-application pressure ratios, and use each of the control curves to control the subway to conduct full-line actual test verification, and obtain the full-line actual test verification results. A selection unit is used to obtain the measured verification results of the entire line, and to select the control curve corresponding to the optimal measured verification results of the entire line, so as to obtain the optimal control parameter scheme for the entire subway line.

[0059] The fully automatic subway smooth operation control device provided in this embodiment of the invention can execute the fully automatic subway smooth operation control method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0060] Example 3 Figure 3 This is a schematic diagram of the structure of the fully automatic subway smooth operation control terminal provided in Embodiment 3 of the present invention; Figure 3 A block diagram is shown that is suitable for implementing embodiments of the present invention. Figure 3 The terminal shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0061] like Figure 3 As shown, terminal 12 is presented in the form of a general-purpose computing device. The components of terminal 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0062] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0063] Terminal 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by terminal 12, including volatile and non-volatile media, removable and non-removable media.

[0064] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Terminal 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 3 Not shown; usually referred to as a "hard drive"). Although Figure 3 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0065] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0066] Terminal 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with terminal 12, and / or with any device that enables terminal 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, terminal 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of terminal 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with terminal 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0067] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the fully automatic subway smooth operation control method provided in the embodiments of the present invention.

[0068] Example 4 Embodiment 4 of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to execute any of the fully automatic subway smooth operation control methods provided in the above embodiments.

[0069] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0070] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0071] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0072] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0073] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A fully automatic subway smooth operation control method, characterized in that, include: The driving skills of subway drivers were tested using an equivalent model, and the test results were obtained. The equivalent model was a wooden stick. The test result was considered qualified if the equivalent model remained stable and did not fall over during the operation of the entire subway line. The driving data of subway drivers whose test results were qualified were collected to obtain sample data; The traction and braking vehicle control curves are obtained by analyzing the sample data, including: summarizing and analyzing the sample data to obtain smooth traction and braking vehicle control curves; sorting out the vehicle control curves to analyze the level change law during start-up acceleration and the timing of electro-pneumatic braking conversion during braking deceleration, and obtaining analysis results; and drawing vehicle control curves with different start-up accelerations and different air brake pre-application pressure ratios based on the analysis results. The subway's operating logic is optimized based on the traction and braking control curves, and the subway is controlled to conduct full-line field tests to determine the optimal control parameter scheme. This includes: selecting control curves with different starting accelerations and different air brake pre-application pressure ratios, and using each control curve to control the subway to conduct full-line field tests to obtain the full-line field test results; obtaining the full-line field test results, and selecting the control curve corresponding to the optimal full-line field test results to obtain the optimal control parameter scheme for the entire subway line. The optimal train control parameter scheme is used to control the automatic operation of the subway.

2. The method according to claim 1, characterized in that, The test of the subway driver's manual driving skills using an equivalent model yielded test results, including: The equivalent model was used to test the manual driving skills of subway drivers; The test result is qualified when the equivalent model with a diameter of 3cm and a height of 20cm does not fall. The test result is good when the equivalent model with a diameter of 4cm and a height of 30cm does not fall. The test result is excellent when the equivalent model with a diameter of 3cm and a height of 30cm does not fall over.

3. The method according to claim 1, characterized in that, The process of collecting driving data from subway drivers whose test results are deemed satisfactory yields sample data, including: An analysis was conducted on the lever level control and speed control of subway drivers whose test results were qualified. The goal is to acquire an excellent subway driver whose control accuracy and speed at the handle level are within 5%, and to enable the excellent subway driver to perform multiple full-line subway operations. Driving data from multiple subway line operations was collected to obtain sample data.

4. The method according to claim 1, characterized in that, The optimal vehicle control parameter scheme includes: The acceleration from a standstill at zero speed on the platform to starting again is 1 m / s². 2 ; The traction level limit within the platform is 40%; During the secondary acceleration after leaving the platform, the traction level increases by 0.1% every 200ms; When braking onto the platform, the air brake pre-pressure ratio is 25% until the subway comes to a stop at zero speed on the platform.

5. A fully automatic subway smooth operation control device, characterized in that, include: The testing module is used to test the driving skills of subway drivers using an equivalent model and obtain test results. The equivalent model is a wooden stick. The test result is qualified if the equivalent model remains stable and does not fall over during the operation of the entire subway line. The data acquisition module is used to collect driving data from subway drivers whose test results are qualified, and obtain sample data. The analysis module is used to analyze the sample data to obtain traction and braking control curves, including: summarizing and parsing the sample data to obtain smooth traction and braking control curves; analyzing the control curves to understand the level change pattern during acceleration and the timing of electro-pneumatic braking transition during deceleration, and obtaining analysis results; and drawing control curves for different starting accelerations and different air brake pre-application pressure ratios based on the analysis results. The optimization module is used to optimize the subway's operating logic based on the traction and braking control curves and control the subway for full-line real-world testing to determine the optimal control parameter scheme, including: selecting control curves for different starting accelerations and different air brake pre-application pressure ratios, and using each control curve to control the subway for full-line real-world testing, obtaining full-line real-world testing results; obtaining the full-line real-world testing results, and selecting the control curve corresponding to the optimal full-line real-world testing results to obtain the optimal control parameter scheme for the entire subway line. The control module is used to control the automatic operation of the subway using the optimal train control parameter scheme.

6. A terminal, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the fully automatic subway smooth operation control method as described in any one of claims 1-4.

7. A storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the fully automatic subway smooth operation control method as described in any one of claims 1-4.

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