A method, device and equipment for determining a subway emergency guarantee braking rate value and a medium

Abstract

A subway emergency guarantee braking rate determination method, device, equipment and medium, relate to the subway engineering design technical field. The basic parameters of the subway project are obtained; the speed margin and the interval margin are obtained by calculating and comparing the basic parameters; the operating type of the working condition is determined according to the speed margin, the interval margin and the basic parameters; if it is a non-normal operating condition, the vehicle basic parameters, the line parameters, the environmental parameters and the signal safety parameters are input into the preset braking safety model to obtain the safety bottom line emergency guarantee braking rate and the initial emergency guarantee braking rate; if it is a normal operating condition, the operating efficiency parameter is taken as a constraint target to iteratively optimize the initial emergency guarantee braking rate to obtain the efficiency adaptive emergency guarantee braking rate; the corresponding emergency guarantee braking rate value list is generated according to the safety bottom line emergency guarantee braking rate and the efficiency adaptive emergency guarantee braking rate. By implementing the method, the problem of conservatism caused by using a single limit working condition calculation is overcome.

Description

Technical Field

[0001] This application relates to the field of subway engineering design technology, specifically to a method, device, equipment and medium for determining the value of the emergency braking rate of a subway. Background Technology

[0002] In urban rail transit, the Emergency Guaranteed Braking Rate (GEBR) is a core safety parameter of the Communication-Based Train Control (CBTC) system. It is defined as the minimum emergency braking deceleration that a train must achieve under the most unfavorable operating conditions. This parameter directly determines the train's safe braking distance, overspeed protection curve, and movement authorization calculation logic, and is a key interface parameter connecting the vehicle's braking system and signal control system.

[0003] In existing technologies, a common train dynamics simulation model is typically used to determine the emergency braking rate. This model imports the vehicle's inherent braking parameters, track gradient parameters, wheel-rail adhesion coefficient, and signal system response delay. Under a uniformly set most unfavorable extreme condition, it performs a single-dimensional numerical simulation of the train braking process, and finally calculates and outputs a fixed emergency braking rate value, which serves as the underlying data for the safety protection of the signal system.

[0004] However, in actual subway operation, trains spend the vast majority of their time in "normal operating conditions"—no faults, regular adhesion, and straight tracks—only facing "abnormal extreme conditions" such as steep gradients, low adhesion, or braking failures in rare cases. Existing simulation models employ a conservative "one-size-fits-all" approach, calculating a single emergency braking rate based entirely on the worst-case extreme conditions. This results in overly conservative calculations of safe protection distances and train tracking intervals by the signaling system under any operating condition, leading to significant waste of line capacity and low operational efficiency. Summary of the Invention

[0005] This application provides a method, device, equipment, and medium for determining the value of the emergency braking rate of a subway. This method overcomes the problem of excessive conservatism caused by the use of a single extreme condition for calculation in the prior art, effectively shortens the train tracking interval, and improves the line capacity and operational efficiency.

[0006] Firstly, this application provides a method for determining the emergency braking rate of a subway system. The method includes: obtaining basic parameters of the subway project, including vehicle basic parameters, environmental parameters, track parameters, signal safety parameters, and operational efficiency parameters; calculating the vehicle basic parameters, track parameters, environmental parameters, and signal safety parameters to obtain the maximum safe permissible speed and minimum safe following interval under the current physical conditions, and comparing the maximum safe permissible speed and minimum safe following interval with the operational efficiency parameters to obtain speed margin and interval margin; determining the operating condition type based on the speed margin, interval margin, vehicle basic parameters, track parameters, and environmental parameters, where the operating condition type includes normal operating conditions and abnormal operating conditions; if the operating condition type is abnormal operating conditions... If the vehicle's basic parameters, route parameters, environmental parameters, and signal safety parameters are input into the preset braking safety model, the safety baseline emergency braking rate is obtained. If the operating condition is normal operating condition, the vehicle's basic parameters, route parameters, environmental parameters, and signal safety parameters are input into the preset braking safety model to obtain the initial emergency braking rate. Using the operating efficiency parameter as the constraint target, the initial emergency braking rate is iteratively optimized to obtain the efficiency-adapted emergency braking rate. Based on the safety baseline emergency braking rate and the efficiency-adapted emergency braking rate, a corresponding list of emergency braking rate values ​​is generated. The list of emergency braking rate values ​​includes the safety baseline emergency braking rate corresponding to abnormal operating conditions and the efficiency-adapted emergency braking rate corresponding to normal operating conditions.

[0007] By adopting the above technical solution, the basic parameters of the subway vehicle, environmental parameters, track parameters, signal safety parameters, and operational efficiency parameters are obtained. The basic parameters of the vehicle, track, environment, and signal safety parameters are calculated to obtain the maximum safe permissible speed and minimum safe following interval under the current physical conditions. The maximum safe permissible speed and minimum safe following interval are then compared with the operational efficiency parameters to obtain speed margin and interval margin. Based on the speed margin, interval margin, basic vehicle parameters, track parameters, and environmental parameters, the operating condition type is determined, achieving accurate identification and classification of normal and abnormal operating conditions. For abnormal operating conditions, the basic vehicle parameters, track parameters, environmental parameters, and signal safety parameters are input into a preset braking safety model to obtain the safety baseline emergency braking rate, ensuring braking even under the most unfavorable conditions. The following measures ensure absolute safety of train braking. For normal operation, vehicle basic parameters, line parameters, environmental parameters, and signal safety parameters are input into a preset braking safety model to obtain an initial emergency braking rate. Then, with operational efficiency parameters as the constraint target, the initial emergency braking rate is iteratively optimized to obtain an efficiency-adaptive emergency braking rate, thereby maximizing operational efficiency while ensuring safety. Based on the safety baseline emergency braking rate and the efficiency-adaptive emergency braking rate, a corresponding list of emergency braking rate values ​​is generated, providing differentiated braking rate parameters for different operating scenarios. This overcomes the overly conservative problem caused by the use of a single extreme condition in existing technologies. While ensuring the safety baseline under abnormal conditions, it fully explores the efficiency optimization space under normal operating conditions, effectively shortening train tracking intervals and improving line capacity and operational efficiency.

[0008] Optionally, the vehicle's basic parameters, track parameters, environmental parameters, and signal safety parameters are calculated to obtain the maximum safe permissible speed and minimum safe overtaking interval under the current physical conditions. The maximum safe permissible speed and minimum safe overtaking interval are then compared with operational efficiency parameters to obtain speed margin and interval margin. Specifically, this includes: obtaining the train's braking phase; calculating the train's travel distance through each braking phase at the initial speed based on the time delay in the vehicle's basic parameters and signal safety parameters, and the gradient resistance and basic resistance in the track parameters and environmental parameters; summing the travel distances of all braking phases to obtain the total safe braking distance at the initial speed; obtaining the maximum permissible braking distance from the track parameters; and using the initial speed as the maximum safe permissible speed when the total safe braking distance equals the maximum permissible braking distance. Speed; determine the safety redundancy distance based on the speed and distance measurement error compensation amount and preset safety margin coefficient in the signal safety parameters; add the total safe braking distance and the safety redundancy distance to obtain the minimum safe protection distance; obtain the stopping time of the preceding vehicle at the platform and the protection travel time of the following vehicle at the target operating speed in the operation efficiency parameters through the minimum safe protection distance; based on the vehicle basic parameters and signal safety parameters, determine the difference between the protection braking establishment time of the following vehicle and the acceleration time of the preceding vehicle to obtain the design time-space difference; add the protection travel time, stopping time and the design time-space difference to obtain the minimum safe tracking interval; subtract the target operating speed in the operation efficiency parameters from the maximum safe allowable speed to obtain the speed margin; subtract the minimum safe tracking interval from the target tracking interval in the operation efficiency parameters to obtain the interval margin.

[0009] By adopting the above technical solution, the braking stages of the train are obtained. Based on the time delay in the vehicle's basic parameters and signal safety parameters, as well as the gradient resistance and basic resistance in the track parameters and environmental parameters, the travel distance of the train through each braking stage at the initial speed is calculated. The travel distances of all braking stages are summed to obtain the total safe braking distance at the initial speed. The maximum permissible braking distance is obtained from the track parameters. When the total safe braking distance equals the maximum permissible braking distance, the initial speed is taken as the maximum safe permissible speed. The safety redundancy distance is determined according to the speed and distance measurement error compensation amount and the preset safety margin coefficient in the signal safety parameters. The total safe braking distance and the safety redundancy distance are added to obtain the minimum safe protection distance. The system obtains the stopping time of the preceding train at the platform and the protective travel time of the following train at the target operating speed in the operational efficiency parameters to pass through the minimum safe protection distance. Combined with the difference between the protective braking establishment time of the following train and the starting acceleration time of the preceding train, which is determined based on the vehicle's basic parameters and signal safety parameters (i.e., the design time-space difference), the protective travel time, stopping time, and design time-space difference are added together to obtain the minimum safe tracking interval. This achieves multi-dimensional comprehensive calculation of train tracking intervals. The speed margin is obtained by subtracting the target operating speed in the operational efficiency parameters from the maximum safe allowable speed, and the interval margin is obtained by subtracting the minimum safe tracking interval from the target tracking interval in the operational efficiency parameters. This ensures that the system can accurately identify efficiency optimization space while ensuring safety.

[0010] Optionally, the operating condition type is determined based on speed margin, interval margin, vehicle basic parameters, track parameters, and environmental parameters. Specifically, this includes: obtaining the braking system fault modes configured in the vehicle basic parameters and obtaining the design wheel-rail adhesion coefficient set in the environmental parameters; if the braking system fault mode is a preset critical fault condition, or the design wheel-rail adhesion coefficient is less than a preset adhesion threshold, then the current simulation condition is determined to be a failure operation condition within the abnormal operating conditions, and a design risk warning is triggered; if the braking system fault mode is a fault-free normal mode, and the design wheel-rail adhesion coefficient is less than a preset adhesion threshold... If the adhesion coefficient is greater than or equal to the preset adhesion threshold, then it is determined whether the speed margin and the interval margin are both greater than the preset minimum physical feasible margin value. If both the speed margin and the interval margin are greater than the preset minimum physical feasible margin value, then the speed margin, interval margin, vehicle basic parameters, line parameters, and environmental parameters are calculated to obtain the comprehensive risk tension index. When the comprehensive risk tension index is greater than the preset risk threshold, the current simulation condition is determined to be an abnormal operating condition. When the comprehensive risk tension index is less than or equal to the preset risk threshold, the current simulation condition is determined to be a normal operating condition.

[0011] By adopting the above technical solution, the braking system fault modes configured in the vehicle's basic parameters and the design wheel-rail adhesion coefficient set in the environmental parameters are obtained. When the braking system fault mode is a preset critical fault condition or the design wheel-rail adhesion coefficient is less than the preset adhesion threshold, the current simulation condition is directly determined to be a failure operation condition in abnormal operation conditions, and design risk warning information is triggered, realizing rapid identification and warning of extreme dangerous conditions. When the braking system fault mode is a fault-free normal mode and the design wheel-rail adhesion coefficient is greater than or equal to the preset adhesion threshold, it is determined whether the speed margin and interval margin are both greater than the preset minimum physical feasibility margin value, thereby eliminating the possibility that the system design itself is physically infeasible. Under the premise that both the margin and the interval margin meet the basic feasibility requirements, the comprehensive risk tension index is calculated by combining the speed margin, interval margin, vehicle basic parameters, line parameters, and environmental parameters. The quantitative assessment of the risk level of the operating condition is realized through the coupled calculation of multi-dimensional parameters. When the comprehensive risk tension index is greater than the preset risk threshold, the current simulation condition is determined to be an abnormal operating condition, while when the comprehensive risk tension index is less than or equal to the preset risk threshold, the current simulation condition is determined to be a normal operating condition. This determination method can quickly identify and respond to extreme dangerous conditions, and can also classify conventional conditions in detail through the comprehensive risk index, avoiding the shortcomings of traditional methods that generalize all conditions.

[0012] Optionally, the preset braking safety model includes a fault degradation correction sub-model, a minimum adhesion constraint sub-model, a train braking dynamics sub-model, and a safety margin superposition sub-model. If the operating condition is an abnormal operating condition, the vehicle basic parameters, track parameters, environmental parameters, and signal safety parameters are input into the preset braking safety model to obtain the safety baseline emergency braking rate. Specifically, this includes: obtaining the braking fault combination mode from the vehicle basic parameters and inputting the braking fault combination mode into the fault degradation correction sub-model to calculate the available braking force output ratio correction value; and then comparing the available braking force output ratio correction value with the train's rated... Multiply the emergency braking decelerations to obtain the degraded braking deceleration; input the wheel-rail adhesion coefficient from the environmental parameters into the minimum adhesion constraint sub-model to calculate the maximum available adhesion limit, and use the maximum available adhesion limit to constrain the amplitude of the degraded braking deceleration, outputting the adhesion-constrained emergency braking deceleration; input the vehicle basic parameters, track parameters, environmental parameters, signal safety parameters, and adhesion-constrained emergency braking deceleration into the train braking dynamics sub-model to obtain the minimum target deceleration required by the train during the constant braking phase; input the minimum target deceleration into the safety margin superposition sub-model to output the safety baseline emergency guarantee braking rate.

[0013] By adopting the above technical solution, the braking fault combination mode in the vehicle's basic parameters is obtained and input into the fault degradation correction sub-model for calculation, resulting in the available braking force output ratio correction value. The available braking force output ratio correction value is multiplied by the train's rated emergency braking deceleration to obtain the degradation braking deceleration. The wheel-rail adhesion coefficient in the environmental parameters is input into the minimum adhesion constraint sub-model to calculate the maximum available adhesion limit. The maximum available adhesion limit is then used to constrain the amplitude of the degradation braking deceleration to output the adhesion constraint emergency braking deceleration, ensuring that the calculation result does not exceed the maximum braking force that the wheel-rail interface can transmit under physical conditions. The vehicle's basic parameters, track parameters, environmental parameters, signal safety parameters, and adhesion constraint emergency braking deceleration are input into the train braking dynamics sub-model to obtain the minimum target deceleration. The minimum target deceleration is input into the safety margin superposition sub-model. The safety margin coefficient, speed and distance measurement error compensation amount, and system response delay error in the signal safety parameters are superimposed on the minimum target deceleration to output the safety bottom line emergency guarantee braking rate. This fully considers system errors and safety redundancy requirements, ensuring the absolute safety and engineering reliability of the braking rate value under abnormal operating conditions.

[0014] Optionally, if the operating condition is normal operating condition, the vehicle basic parameters, track parameters, environmental parameters, and signal safety parameters are input into the preset braking safety model to obtain the initial emergency braking rate. Specifically, this includes: inputting the normal fault-free mode from the vehicle basic parameters into the fault degradation correction sub-model, determining the available braking force output ratio correction value as a standard lossless coefficient, and multiplying the standard lossless coefficient by the train's rated emergency braking deceleration to output the standard emergency braking deceleration; inputting the wheel-rail adhesion coefficient under normal conditions from the environmental parameters into the minimum adhesion constraint sub-model, calculating the normal adhesion limit, and using the normal adhesion limit to constrain the amplitude of the standard emergency braking deceleration to output the standard adhesion constraint emergency braking deceleration; inputting the vehicle basic parameters, track parameters, environmental parameters, signal safety parameters, and the standard adhesion constraint emergency braking deceleration into the train braking dynamics sub-model to obtain the standard target deceleration required by the train under normal operating conditions; and inputting the standard target deceleration into the safety margin superposition sub-model to output the initial emergency braking rate.

[0015] By adopting the above technical solution, the normal fault-free mode in the vehicle's basic parameters is input into the fault degradation correction sub-model to determine the standard lossless coefficient as the correction value of the available braking force output ratio. The standard lossless coefficient is then multiplied by the train's rated emergency braking deceleration to output the standard emergency braking deceleration. The wheel-rail adhesion coefficient under normal conditions in the environmental parameters is input into the minimum adhesion constraint sub-model to calculate the normal adhesion limit, and the standard adhesion constraint emergency braking deceleration is output. The vehicle's basic parameters, track parameters, environmental parameters, signal safety parameters, and the standard adhesion constraint emergency braking deceleration are input into the train braking dynamics sub-model to obtain the standard target deceleration required by the train under normal operating conditions. The standard target deceleration is input into the safety margin superposition sub-model, and the standard safety margin coefficient, standard speed and distance measurement error compensation amount, and standard system response delay error corresponding to the signal safety parameters are superimposed on the standard target deceleration to output the initial emergency protection braking rate. Compared with abnormal operating conditions, a moderate safety margin parameter is adopted instead of an extremely conservative value, reserving space for subsequent efficiency optimization while ensuring safety, and effectively balancing the dual needs of safety assurance and efficiency improvement.

[0016] Optionally, with operational efficiency parameters as the constraint target, the initial emergency braking rate is iteratively optimized to obtain an efficiency-adaptive emergency braking rate. Specifically, this includes: using the initial emergency braking rate as the current iterative braking rate and setting the braking rate adjustment step size; recalculating and summing the current travel distance of the train at each braking stage based on the current iterative braking rate, vehicle basic parameters, track parameters, and environmental parameters to obtain the current iterative safe braking distance; determining the current iterative safe redundancy distance based on the speed and distance measurement error compensation amount and the preset safety margin coefficient in the signal safety parameters, and adding the current iterative safe braking distance to the current iterative safe redundancy distance to obtain the current iterative minimum safe protection distance; dividing the current iterative minimum safe protection distance by the target operating speed to obtain the current iterative protection travel time; and calculating the current iterative protection travel time... The current iteration tracking interval is obtained by adding the stopping time of the preceding train at the platform and the designed time-space difference. If the current iteration tracking interval is less than or equal to the target tracking interval in the operation efficiency parameters, and the current maximum safe allowable speed is greater than or equal to the target operating speed, then the current iteration braking rate is determined as the efficiency-adaptive emergency braking rate. The current maximum safe allowable speed is derived from the current iteration braking rate. If the current iteration tracking interval is greater than the target tracking interval in the operation efficiency parameters, or the current maximum safe allowable speed is less than the target operating speed, then the current iteration braking rate is cumulatively adjusted using the braking rate adjustment step size, and the adjusted braking rate is used as the new current iteration braking rate. The process returns to recalculate the current iteration safe braking distance until the constraint conditions are met, and the corresponding efficiency-adaptive emergency braking rate is output.

[0017] By adopting the above technical solution, the current travel distance of the train in each braking stage is recalculated based on the current iterative braking rate, vehicle basic parameters, track parameters, and environmental parameters, and the current iterative safe braking distance is obtained by summing them. The current iterative safe redundancy distance is determined based on the speed and distance measurement error compensation amount and the preset safety margin coefficient in the signal safety parameters. The current iterative safe braking distance and the current iterative safe redundancy distance are then added to obtain the current iterative minimum safe protection distance, realizing the dynamic correlation calculation between braking rate changes and safe protection distance. The current iterative protection travel time is obtained by dividing the current iterative minimum safe protection distance by the target operating speed. The current iteration tracking interval is obtained by adding the approach time, the stop time of the preceding train at the platform, and the design time-space difference. If the constraint conditions are met, the current iteration braking rate is determined as the efficiency-adaptive emergency braking rate, thus achieving the convergence output of the optimization target. If the current iteration tracking interval is greater than the target tracking interval in the operation efficiency parameters, or the current maximum safe allowable speed is less than the target operating speed, the braking rate adjustment step size is used to cumulatively adjust the current iteration braking rate, and the adjusted braking rate is used as the new current iteration braking rate for recalculation. The braking rate is gradually increased to gradually meet the operation efficiency constraints, which significantly improves the line throughput capacity and operation efficiency under normal operating conditions.

[0018] Optionally, a list of emergency braking rate values ​​is generated based on the safety baseline emergency braking rate and the efficiency adaptation emergency braking rate. Specifically, this includes: obtaining a historical dataset of similar projects identical to the metro project from a historical project database. This historical dataset includes historical input parameters, historical safety baseline emergency braking rates, and historical efficiency adaptation emergency braking rates. Clustering analysis algorithms are used to cluster the historical dataset to determine reasonable ranges for historical safety baseline braking rates and historical efficiency adaptation braking rates. If both the safety baseline emergency braking rate and the efficiency adaptation emergency braking rate are within their respective reasonable ranges, the verification is considered successful, and a list of emergency braking rate values ​​is generated based on these values. If either the safety baseline emergency braking rate or the efficiency adaptation emergency braking rate is outside its reasonable range, the verification is considered abnormal, and the basic parameters are recalculated based on the historical dataset until the verification is successful and the corresponding list of emergency braking rate values ​​is output.

[0019] By adopting the above technical solution, a dataset of similar historical projects with similar environments and parameters to the current subway project is retrieved from the historical project database. Cluster analysis algorithms are used to construct reasonable ranges for historical safety baseline braking rates and historical efficiency adaptation braking rates. The safety baseline emergency braking rate and efficiency adaptation emergency braking rate obtained from the current simulation calculation are double-boundary verified to ensure that the values ​​fall within the acceptable range of industry experience. This effectively avoids abnormal values ​​caused by input parameter errors. When the verification finds that the safety baseline emergency braking rate or efficiency adaptation emergency braking rate exceeds the historical reasonable range, the intelligent correction process based on the historical similar project dataset is immediately initiated, and the simulation calculation steps are re-executed to form a closed-loop iterative optimization until the verification is passed, completely replacing the inefficient mode that requires repeated manual trial and error adjustments.

[0020] The second aspect of this application provides a device for determining the emergency braking rate value of a subway system. The device includes an acquisition unit, a working condition classification unit, a simulation calculation unit, and an output unit. The acquisition unit acquires basic parameters of the subway project, including vehicle basic parameters, environmental parameters, track parameters, signal safety parameters, and operational efficiency parameters. The working condition classification unit calculates the vehicle basic parameters, track parameters, environmental parameters, and signal safety parameters to obtain the maximum safe permissible speed and minimum safe following interval under the current physical conditions, and compares the maximum safe permissible speed and minimum safe following interval with the operational efficiency parameters to obtain speed margin and interval margin. The device determines the operating condition type based on the speed margin, interval margin, vehicle basic parameters, track parameters, and environmental parameters. The operating condition type includes normal operating conditions and abnormal operating conditions. The simulation calculation unit... The calculation unit, if the operating condition is an abnormal operating condition, inputs the vehicle's basic parameters, route parameters, environmental parameters, and signal safety parameters into a preset braking safety model to obtain the safety baseline emergency braking rate; if the operating condition is a normal operating condition, it inputs the vehicle's basic parameters, route parameters, environmental parameters, and signal safety parameters into the preset braking safety model to obtain the initial emergency braking rate; using the operating efficiency parameter as a constraint target, iteratively optimizes the initial emergency braking rate to obtain the efficiency-adapted emergency braking rate; the output unit generates a corresponding emergency braking rate value list based on the safety baseline emergency braking rate and the efficiency-adapted emergency braking rate. The emergency braking rate value list includes the safety baseline emergency braking rate corresponding to the abnormal operating condition and the efficiency-adapted emergency braking rate corresponding to the normal operating condition.

[0021] In a third aspect, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory, causing the electronic device to perform any of the methods described above in this application.

[0022] In a fourth aspect, this application provides a computer-readable storage medium storing instructions that, when executed, perform any of the methods described above in this application.

[0023] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. Obtain basic vehicle parameters, environmental parameters, track parameters, signal safety parameters, and operational efficiency parameters for the subway project. Calculate the maximum safe permissible speed and minimum safe following interval under the current physical conditions. Compare the maximum safe permissible speed and minimum safe following interval with the operational efficiency parameters to obtain speed margin and interval margin. Determine the operating condition type based on the speed margin, interval margin, vehicle basic parameters, track parameters, and environmental parameters, achieving accurate identification and classification of normal and abnormal operating conditions. For abnormal operating conditions, input the vehicle basic parameters, track parameters, environmental parameters, and signal safety parameters into a preset braking safety model to obtain the safety baseline emergency braking rate, ensuring train braking under the most unfavorable conditions. For the absolute guarantee of safety, and for normal operation, the vehicle's basic parameters, line parameters, environmental parameters, and signal safety parameters are input into the preset braking safety model to obtain the initial emergency braking rate. Then, with the operational efficiency parameter as the constraint target, the initial emergency braking rate is iteratively optimized to obtain the efficiency-adapted emergency braking rate, thereby maximizing operational efficiency while ensuring safety. Based on the safety baseline emergency braking rate and the efficiency-adapted emergency braking rate, a corresponding list of emergency braking rate values ​​is generated, providing differentiated braking rate parameters for different operating scenarios. This overcomes the overly conservative problem caused by the use of a single extreme condition for calculation in existing technologies. While ensuring the safety baseline under abnormal operating conditions, it fully explores the efficiency optimization space under normal operating conditions, effectively shortening the train tracking interval and improving line capacity and operational efficiency. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the first process of a method for determining the emergency braking rate of a subway, provided in an embodiment of this application. Figure 2 This is a schematic diagram of the first process of a method for determining the emergency braking rate of a subway, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0025] Explanation of reference numerals in the attached figures: 300, electronic device; 301, processor; 302, memory; 303, user interface; 304, network interface; 305, communication bus. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0027] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0028] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0029] Therefore, overcoming the overly conservative nature of existing technologies that rely on single extreme conditions for calculation, and thus improving line capacity and operational efficiency, is a pressing issue. This application provides a method for determining the emergency braking rate value for subway systems, applied in an integrated simulation terminal. This integrated simulation terminal serves as a platform for providing emergency braking rate value determination services for subway projects. Figure 1 This is a schematic diagram of the first process of a method for determining the emergency braking rate of a subway system, as provided in an embodiment of this application. (Refer to...) Figure 1 The method includes the following steps S101-S107.

[0030] S101: Obtain basic parameters for the subway project, including vehicle basic parameters, environmental parameters, track parameters, signal safety parameters, and operational efficiency parameters.

[0031] In S101 above, to ensure the accuracy and reliability of the calculation of the emergency braking rate for the subway, considering the problems in the traditional GEBR calculation process, such as inconsistencies in parameter standards between vehicle and signal manufacturers, ambiguous definition of operating condition boundaries, and susceptibility to errors in manual input, the simulation tool is installed on a lightweight portable industrial control terminal or desktop platform for rapid on-site deployment. This portable industrial control terminal can be a lightweight portable industrial control host or a conventional desktop / laptop computer. Its configuration must meet the requirements of numerical simulation and data processing, and it must also have data storage, interface integration, and report printing functions, with a built-in encryption module to ensure the security of project parameters and simulation data. This design supports offline operation, enabling the tool to be used in engineering sites without network access, without relying on high-end servers, thus significantly lowering the barrier to on-site use and ensuring that designers can quickly conduct GEBR calculation simulations in various scenarios such as construction sites and conference rooms.

[0032] Once the hardware is ready, a standardized input module is used to obtain the basic parameters of the subway project. This module employs a templated input interface with built-in parameter entry tables in a fixed format, forcibly unifying input condition boundaries and fundamentally eliminating errors caused by manually customized definitions. Basic parameters include vehicle basic parameters, environmental parameters, track parameters, signal safety parameters, and operational efficiency parameters. The vehicle basic parameter submodule uses standardized parameter templates to guide operators in entering inherent vehicle parameters such as train formation type, axle load, number of bogies, braking unit configuration, maximum gross vehicle weight (AW3), unloaded weight (AW0), common braking deceleration, and emergency braking rated deceleration. It also supports selecting braking fault combination modes, including industry-standard fault conditions such as single bogie failure, 50% brake cutoff, and single-car brake failure, and inputting timing parameters such as brake establishment delay and traction cutoff delay. The environmental parameters section requires the input of the wheel-rail adhesion coefficient, with preset default values ​​for dry, wet / slippery, and icy / snowy conditions. It also supports custom input to adapt to special climatic conditions and requires the input of the environmental drag coefficient to reflect the impact of air resistance during train operation. Regarding track parameters, operators need to input track design parameters, including geometric characteristic parameters such as maximum gradient, minimum curve radius, distribution of tunnel / elevated / ground sections, and straight track reference. These parameters directly affect the calculation of additional resistance and braking distance during train operation.

[0033] The signal safety parameter submodule adopts a common safety indicator template for the urban rail transit industry, and inputs signal-side constraint parameters such as ATP system response delay, speed and distance measurement error, safety margin coefficient, IEEE 1474.1 standard constraint threshold, and five-stage braking delay. The five-stage braking delay is further divided into time parameters for the response stage, traction cut-off stage, coasting stage, braking establishment stage, and constant braking stage to adapt to the safety protection logic of mainstream signaling systems. The operational efficiency parameter submodule requires input of operational efficiency-specific parameters such as the design minimum tracking interval, design operating speed, platform dwell time, and headway control threshold. These parameters will be used for the linkage verification between GEBR and operational efficiency under normal operating conditions, while these parameters will be automatically masked under abnormal operating conditions to ensure that efficiency indicators do not interfere with extreme safety conditions.

[0034] To further improve input accuracy and efficiency, AI / big data analytics is integrated into the standardized input module as an intelligent enabling unit. This unit leverages a built-in historical project database and a large sample of GEBR values ​​from similar industry lines to build an AI analysis model, performing the following core functions in real time during parameter input: First, intelligent parameter verification and completion: the AI ​​model screens manually entered parameters for reasonableness, automatically identifying abnormal values ​​and missing parameters by comparing them with parameter ranges and physical constraints in the historical database. Second, historical data association and iteration: it can retrieve input parameters and final GEBR values ​​from similar historical projects, enabling intelligent reuse of parameter templates. Simultaneously, it accumulates and optimizes the AI ​​model using new project data, forming a closed-loop big data iteration process in the input stage.

[0035] For example, during the design phase of a certain city's Metro Line 3 project, designers used portable industrial control laptops to start simulation tools in the project site conference room. They sequentially entered the following parameters through standardized input modules: For basic vehicle parameters, they entered a 6-car B-type trainset, a single-car axle load of 14 tons, 8 bogies, a gross vehicle mass of 294 tons in AW3 condition, a gross vehicle mass of 198 tons in AW0 condition, a rated emergency braking deceleration of 1.2 m / s², and a braking settling time of 2.5 seconds; for environmental parameters, they selected an adhesion coefficient of 0.3 under normal dry conditions and an adhesion coefficient of 0.3 under extreme wet and slippery conditions. The coefficient of friction is 0.15, and the environmental drag coefficient is taken as the standard value for a speed of 80km / h. The line parameters entered include a maximum gradient of 30‰, a minimum curve radius of 350 meters, and 80% of the line being underground and 20% being elevated. The signal safety parameters entered include an ATP response delay of 1.0 second, a speed measurement error of ±2%, a safety margin coefficient of 1.15, and five-stage delays of 0.8 / 1.2 / 0.5 / 2.5 / steady-state seconds, respectively. The operational efficiency parameters entered include a minimum design tracking interval of 90 seconds, a design operating speed of 80km / h, and a station stop time of 30 seconds. During the data entry process, the AI ​​verification module automatically detected a deviation between the braking establishment time of 2.5 seconds and the typical value of 2.3 seconds for B-type trains in the system database. It immediately popped up a prompt and provided a correction suggestion. After verification, the designers found that it was an input error and promptly corrected it to 2.3 seconds.

[0036] S102: Calculate the vehicle's basic parameters, line parameters, environmental parameters, and signal safety parameters to obtain the maximum safe permissible speed and minimum safe tracking interval under the current physical conditions. Then, compare the maximum safe permissible speed and minimum safe tracking interval with the operating efficiency parameters to obtain the speed margin and interval margin.

[0037] In step S102 above, the vehicle's basic parameters, track parameters, environmental parameters, and signal safety parameters are calculated to obtain the maximum safe permissible speed and minimum safe following interval under the current physical conditions. These parameters are then compared with operational efficiency parameters to obtain speed margin and interval margin. Specifically, this includes: obtaining the train's braking phase; calculating the train's travel distance through each braking phase at the initial speed based on the time delay in the vehicle's basic parameters and signal safety parameters, and the gradient resistance and basic resistance in the track parameters and environmental parameters; summing the travel distances of all braking phases to obtain the total safe braking distance at the initial speed; obtaining the maximum permissible braking distance from the track parameters; and using the initial speed as the maximum safe braking distance when the total safe braking distance equals the maximum permissible braking distance. Permissible speed; determine the safety redundancy distance based on the speed and distance measurement error compensation and preset safety margin coefficient in the signal safety parameters; add the total safe braking distance to the safety redundancy distance to obtain the minimum safe protection distance; obtain the stopping time of the preceding vehicle at the platform and the protection travel time of the following vehicle at the target operating speed in the operation efficiency parameters through the minimum safe protection distance; based on the vehicle's basic parameters and signal safety parameters, determine the difference between the protection braking establishment time of the following vehicle and the acceleration time of the preceding vehicle to obtain the design time-space difference; add the protection travel time, stopping time, and design time-space difference to obtain the minimum safe tracking interval; subtract the target operating speed in the operation efficiency parameters from the maximum safe permissible speed to obtain the speed margin; subtract the minimum safe tracking interval from the target tracking interval in the operation efficiency parameters to obtain the interval margin.

[0038] Specifically, according to the IEEE 1474.1 international standard and domestic urban rail transit engineering design specifications, the emergency braking process of a train is strictly divided into five consecutive stages: response stage, traction cut-off stage, coasting stage, brake establishment stage, and constant braking stage. In the response stage, from receiving the emergency braking command to the driver or automatic system initiating the operation, the train maintains its original operating state. The duration of this stage is directly taken from the ATP system response delay recorded in the signal safety parameters. In the traction cut-off stage, the train's traction system is de-energized but braking force has not yet been applied. The train coasts due to inertia and is subject to track resistance. The duration of this stage is taken from the traction cut-off delay in the vehicle's basic parameters. The coasting stage follows immediately, where the train continues to coast without traction or braking, only subject to basic resistance and gradient resistance. In the brake establishment stage, the braking system begins to apply braking force but has not yet reached the rated value. The braking force gradually increases from zero to the design value. The duration of this stage is taken from the brake establishment delay in the vehicle's basic parameters. The constant braking phase is the process in which the train continuously decelerates until it stops under the rated emergency braking force, during which the braking force remains at a constant maximum value.

[0039] Based on the above five-stage definition, the travel distance of the train through each braking stage is calculated under the assumed initial speed. In the response and traction cut-off stages, the train speed remains essentially constant or decreases slowly. A uniform or quasi-uniform speed model is used, multiplying the duration of each stage by the current speed and adding the effects of basic resistance and gradient resistance to calculate the travel distance. The basic resistance coefficient is obtained from the environmental drag coefficient in the environmental parameters. Gradient resistance is calculated based on the maximum gradient value in the line parameters, with the additional resistance component calculated. When the resistance direction is opposite to the train's direction of motion, it is a downhill condition and resistance needs to be deducted; when the resistance direction is the same as the direction of motion, it is an uphill condition and resistance needs to be added. In the coasting stage, the train decelerates only under the influence of resistance. A motion equation is established based on Newton's second law, considering the total mass of the train in the vehicle's basic parameters, the drag coefficient in the environmental parameters, and the gradient resistance in the line parameters. The speed decay curve and displacement integral value for this stage are solved using numerical integration. In the braking establishment stage, the braking force increases linearly from zero to the rated value. A variable acceleration model is used, with the braking force time function being a linearly increasing function. The deceleration process and travel distance for this stage are calculated by combining the effects of resistance. The calculation of the constant braking phase is the most critical. The rated emergency braking deceleration is extracted from the vehicle's basic parameters, and the constant braking force is calculated in combination with the total mass of the train. Then, the basic resistance and gradient resistance are superimposed to establish the constant acceleration motion equation. The distance required for the train to decelerate to zero from the end of the braking process is calculated using kinematic formulas.

[0040] After calculating the travel distance for each stage, the travel distances for the response stage, traction cut-off stage, coasting stage, braking establishment stage, and constant braking stage are sequentially summed to obtain the total safe braking distance at that initial speed. The total safe braking distance represents the total physical distance the train travels from receiving an emergency braking command to coming to a complete stop, and is a core indicator for evaluating train braking performance. The maximum permissible braking distance is retrieved from the track parameters. This parameter is typically determined by the track designer based on factors such as station spacing, block section length, and safety protection requirements, representing the maximum allowable braking space under track conditions. Through an iterative algorithm, the initial speed value is continuously adjusted, and the above five-stage travel distance calculation is repeated. When the total safe braking distance corresponding to a certain initial speed is exactly equal to the maximum permissible braking distance, that initial speed is determined as the maximum safe permissible speed under the current physical conditions. This speed value represents the maximum speed limit under existing vehicle braking capacity, track conditions, and environmental constraints that allows the train to operate safely without exceeding the braking distance limit, providing a physical boundary benchmark for subsequent speed margin calculations.

[0041] After determining the maximum safe permissible speed, the minimum safe tracking interval is further calculated to assess the line's capacity. Speed ​​and distance measurement error compensation and a preset safety margin coefficient are extracted from the signal safety parameters. The speed and distance measurement error compensation is used to compensate for the cumulative error between the train speed measurement system and the track circuit distance measurement system, while the preset safety margin coefficient adds additional safety redundancy based on the theoretical calculation value to cope with emergencies. The speed and distance measurement error compensation and the total safe braking distance are superimposed according to the preset safety margin coefficient to obtain the safety redundancy distance. The total safe braking distance and the safety redundancy distance are added together to obtain the minimum safe protection distance. This distance represents the minimum space margin required to ensure safe braking of the train and is the core basis for setting the movement authorization endpoint in the signal system.

[0042] To calculate the tracking interval, it is necessary to simulate the running sequence relationship between the preceding and following vehicles on the track. The station dwell time of the preceding vehicle at the platform is obtained from the operational efficiency parameters, which reflect the fixed dwell time required for passenger boarding and alighting. Simultaneously, the protective travel time required for the following vehicle to travel at a constant speed through the minimum safety protection distance at the target operating speed in the operational efficiency parameters is calculated by dividing the minimum safety protection distance by the target operating speed. This protective travel time represents the time required for the following vehicle to travel from the departure position of the preceding vehicle to the safety protection boundary. Based on the vehicle's basic parameters and signal safety parameters, the difference between the protective braking establishment time of the following vehicle and the acceleration time of the preceding vehicle is determined, yielding the design time-space difference. The protective braking establishment time of the following vehicle includes the sum of ATP response delay, traction cut-off delay, and braking establishment delay. The acceleration time of the preceding vehicle is calculated based on the traction characteristic curve in the vehicle's basic parameters, determining the time required for the train to accelerate from a standstill to the target operating speed. The introduction of the design time-space difference considers the time difference between the acceleration of the preceding vehicle and the emergency braking establishment process of the following vehicle, ensuring the accuracy of the tracking interval calculation. The minimum safe tracking interval is obtained by summing the protective travel time, station dwell time, and design time-space difference. This interval represents the minimum time interval required for two trains to maintain a safe distance under current physical conditions and safety constraints, and directly determines the maximum throughput capacity of the line.

[0043] After calculating the maximum safe permissible speed and the minimum safe tracking interval, these two physical safety boundary values ​​are quantitatively compared with the design targets in the operational efficiency parameters to assess the optimization space of the current parameter combination. The speed margin is obtained by subtracting the target operating speed from the operational efficiency parameters. A positive speed margin indicates that the maximum speed allowed by current physical conditions exceeds the operational demand speed, indicating sufficient speed optimization space or safety margin; a negative speed margin indicates that the target operating speed exceeds the physical safety permissible range, requiring adjustment of the GEBR or operating speed to meet safety constraints. Simultaneously, the interval margin is obtained by subtracting the minimum safe tracking interval from the target tracking interval in the operational efficiency parameters. A positive interval margin indicates that the minimum tracking interval under current physical conditions is less than the operational target interval, the line capacity meets the demand, and there is room for efficiency optimization; a negative interval margin indicates that the target tracking interval is too aggressive and cannot be achieved under current physical conditions, requiring optimization of the GEBR or adjustment of the operational interval target to balance safety and efficiency.

[0044] Through the above calculations and comparisons, the dual indicators of speed margin and interval margin enable the system to accurately identify whether the current parameter combination has the conditions for efficiency optimization or whether there are safety hazards that need to be adjusted, thereby guiding the system to select the correct GEBR calculation strategy and achieve a dynamic balance between safety and efficiency.

[0045] For example, taking Metro Line 5 of a certain city as an example, the parameters entered for this line include: basic vehicle parameters are 6-car A-type trains, AW3 state total mass of 310 tons, rated emergency braking deceleration of 1.0 m / s², braking establishment delay of 2.8 seconds, and traction cut-off delay of 1.5 seconds; line parameters are maximum gradient of 35‰ uphill condition and maximum allowable braking distance of 800 meters; environmental parameters are adhesion coefficient of 0.28 under normal dry conditions and environmental drag coefficient taken at a speed of 90 km / h; signal safety parameters are ATP response delay of 1.2 seconds, speed and distance measurement error compensation of 15 meters, and safety margin coefficient of 1.12; and operational efficiency parameters are target operating speed of 90 km / h (25 m / s), target tracking interval of 120 seconds, and platform dwell time of 35 seconds. First, set the initial speed to 25 m / s and calculate the travel distance in five stages: In the response stage, with a 1.2-second ATP delay, the train travels 30 meters at a constant speed of 25 m / s; in the traction cut-off stage, with a 1.5-second delay, considering wind resistance and 35‰ uphill resistance, the train decelerates to 24.2 m / s, traveling 37 meters; in the coasting stage, with a 0.5-second delay, the train continues to decelerate to 23.8 m / s, traveling 12 meters; in the braking establishment stage, with a 2.8-second delay, the braking force increases linearly, with an average deceleration of 0.5 m / s², the speed drops to 22.4 m / s, and the travel distance is 65 meters; in the constant braking stage, the rated braking deceleration of 1.0 m / s² is superimposed with the additional 35‰ uphill resistance of 0.35 m / s², resulting in a combined deceleration of 1.35 m / s², requiring 16.6 seconds to decelerate from 22.4 m / s to zero, traveling 186 meters. The total safe braking distance for the five stages is 30 + 37 + 12 + 65 + 186 = 330 meters, far less than the maximum permissible braking distance of 800 meters. Increasing the initial speed to 35 m / s and recalculating, the total distance for the five stages increases to 580 meters, still less than 800 meters. Continuing the iteration, when the initial speed is increased to 42 m / s (i.e., a speed of 151 km / h), the total distance for the five stages reaches 798 meters, close to the 800-meter limit. Through fine-tuning, it was finally determined that the total safe braking distance is exactly 800 meters when the initial speed is 41.8 m / s (i.e., a speed of 150.5 km / h). Therefore, the maximum permissible safe speed is 150.5 km / h. The calculated safety redundancy distance is 330 meters × 0.12 + 15 meters = 54.6 meters, and the minimum safe protection distance is 330 + 54.6 = 384.6 meters. The following vehicle travels at 25 m / s through a 384.6-meter protective barrier in 384.6 / 25 = 15.4 seconds. The braking time for the following vehicle is 1.2 + 1.5 + 2.8 = 5.5 seconds. The preceding vehicle accelerates from a standstill to 25 m / s in 18 seconds. The designed time difference is 5.5 - 18 = -12.5 seconds, meaning the preceding vehicle starts 12.5 seconds in advance. The minimum safe following interval is 15.4 + 35 + (-12.5) = 37.9 seconds. The speed margin is 150.5 - 90 = 60.5 km / h, and the interval margin is 120 - 37.9 = 82.1 seconds.The results show that under the current parameter combination, the maximum safe allowable speed of the line is much higher than the target operating speed, and the minimum safe tracking interval is much smaller than the target tracking interval. Both the speed margin and the interval margin are significantly positive, indicating that there is ample room for safety and efficiency optimization under the current physical conditions, and the line can enter the normal operating condition efficiency-adaptive GEBR optimization process.

[0046] S103: Determine the operating condition type based on speed margin, interval margin, vehicle basic parameters, line parameters, and environmental parameters.

[0047] In S103 above, the operating condition type is determined based on speed margin, interval margin, vehicle basic parameters, track parameters, and environmental parameters. Specifically, this includes: obtaining the braking system fault mode configured in the vehicle basic parameters and obtaining the design wheel-rail adhesion coefficient set in the environmental parameters; if the braking system fault mode is a preset critical fault condition, or the design wheel-rail adhesion coefficient is less than a preset adhesion threshold, then the current simulation condition is determined to be a failure operation condition in abnormal operation conditions, and design risk warning information is triggered; if the braking system fault mode is a fault-free normal mode, and the design... If the wheel-rail adhesion coefficient is greater than or equal to the preset adhesion threshold, then it is determined whether the speed margin and the interval margin are both greater than the preset minimum physical feasible margin value. If both the speed margin and the interval margin are greater than the preset minimum physical feasible margin value, then the speed margin, interval margin, vehicle basic parameters, track parameters, and environmental parameters are calculated to obtain the comprehensive risk tension index. When the comprehensive risk tension index is greater than the preset risk threshold, the current simulation condition is determined to be an abnormal operating condition. When the comprehensive risk tension index is less than or equal to the preset risk threshold, the current simulation condition is determined to be a normal operating condition.

[0048] Specifically, the system retrieves the braking system fault modes configured in the vehicle's basic parameters from the standardized input module. These fault modes are selected or customized by the operator from the built-in operating condition library based on project requirements during input. The braking system fault modes cover a variety of industry-standard failure scenarios, including progressive fault combinations such as single bogie brake failure, 50% brake unit cutoff, complete single-car brake failure, and simultaneous failure of multiple bogies. The system has a built-in preset critical fault condition judgment logic, defining fault modes with a braking capacity reduction exceeding 30% as critical faults. These faults significantly weaken train braking performance, potentially causing the train to fail to meet braking distance requirements for normal operation even under ideal track and environmental conditions, and must be included in the abnormal operating condition category for extreme safety verification. Through string matching and parameter threshold comparison algorithms, the currently input braking system fault modes are compared one by one with the preset critical fault condition library to determine whether critical failure characteristics exist.

[0049] The design wheel-rail adhesion coefficient is extracted from environmental parameters. This coefficient is entered with distinctions between dry, wet / slippery, and icy / snowy conditions, or is user-defined. The wheel-rail adhesion coefficient directly determines the maximum braking force that the wheel-rail contact surface can transmit, representing the physical upper limit of train braking performance. A preset adhesion threshold is included, using an adhesion coefficient of 0.20 as the dividing line between normal operating conditions and extreme conditions, based on urban rail transit industry design specifications and the IEEE 1474.1 standard. When the wheel-rail adhesion coefficient is below 0.20, it indicates that the rail surface is severely wet / slippery, covered in ice / snow, or contaminated with oil. Even with full braking force, insufficient adhesion may cause wheel-rail slippage or even wheel spin, resulting in actual braking deceleration far below the design value. This must be assessed under abnormal extreme conditions. Numerical comparison calculations determine whether the current design wheel-rail adhesion coefficient is less than the preset adhesion threshold of 0.20.

[0050] When the braking system failure mode is detected as a preset critical failure condition, or the designed wheel-rail adhesion coefficient is less than the preset adhesion threshold of 0.20, the current simulation condition is determined to be a failure operation condition within the abnormal operation conditions. Failure operation conditions represent the most unfavorable operating state of the train under equipment failure or extreme environmental conditions. Under such conditions, the braking capacity of the train is severely weakened, and efficiency optimization according to normal operating standards is impossible; therefore, safe braking capacity must be the sole consideration. Upon determining the failure operation condition, a design risk warning message is immediately triggered. This warning message is presented in various forms, including pop-ups, warning sounds, and report annotations, clearly informing operators that the current condition belongs to an extreme safety scenario. Subsequent GEBR calculations will automatically mask operating efficiency parameters and only output the guaranteed GEBR safety baseline value. The warning information also details the specific reasons for triggering the failure determination, such as "Single bogie brake failure detected, braking capacity reduced by 37.5%, failure operation condition triggered" or "Wheel-rail adhesion coefficient detected below threshold 0.20 (0.15), failure operation condition triggered," enabling operators to clearly understand the basis for the operation condition classification, facilitating subsequent parameter adjustments and scheme optimization. This automated failure condition identification and warning mechanism effectively avoids omissions or misjudgments that may occur in traditional manual judgment, ensuring that all extreme safety scenarios are included in abnormal operation conditions for special assessment.

[0051] When the braking system failure mode is a fault-free normal mode, and the designed wheel-rail adhesion coefficient is greater than or equal to the preset adhesion threshold of 0.20, it indicates that the train braking system is in normal working condition, and the rail adhesion conditions also meet the requirements of normal operation. At this time, the operating condition cannot be directly determined as a normal operating condition, and further verification of physical feasibility and risk tension level is required. The calculated speed margin and interval margin are retrieved, and it is determined whether both margin indicators are greater than the preset minimum physical feasibility margin value. The preset minimum physical feasibility margin value is set to 0. The physical meaning of this threshold is as follows: a speed margin greater than 0 indicates that the maximum safe allowable speed is higher than the target operating speed, indicating that the train can safely reach the operating speed requirement under the current braking capacity and track conditions; an interval margin greater than 0 indicates that the minimum safe tracking interval is less than the target tracking interval, indicating that the track capacity can meet the operating interval requirements. Only when both the speed margin and interval margin are positive values ​​can it be said that the current parameter combination is physically feasible to achieve the operating target. Otherwise, even if the braking system is fault-free and the adhesion conditions are normal, safety hazards will arise because the physical boundary constraints cannot meet the operating requirements.

[0052] When both the speed margin and the spacing margin are greater than 0, although the physical feasibility has been initially verified, it is still necessary to assess the risk tension level of the parameter combination and determine whether there are potential safety pressures or efficiency conflicts. The comprehensive risk tension index is obtained by calculating speed margin, interval margin, vehicle basic parameters, track parameters, and environmental parameters. Specifically, this includes: processing speed margin and interval margin with inverse proportional functions to obtain speed tension components and interval tension components; weighting and summing the speed tension components and interval tension components to obtain the dynamic margin tension index; selecting braking system design availability parameters from vehicle basic parameters and mapping them through a first mapping function to obtain a health risk score; selecting the absolute value of the design maximum gradient and the reciprocal of the design minimum curve radius from track parameters and mapping them through a second mapping function to obtain a track risk score; selecting the design wheel-rail adhesion coefficient value from environmental parameters and mapping it through a third mapping function to obtain an environmental risk score; weighting and summing the health risk score, track risk score, and environmental risk score to obtain a static inherent tension index; and weighting and summing the dynamic margin tension index and the static inherent tension index to obtain the comprehensive risk tension index.

[0053] Specifically, speed margin and interval margin are extracted. These two indicators represent the distance between the operational target and the physical boundary under the current parameter combination. A positive and large speed margin indicates that the operating speed is far from the physical upper limit, with sufficient safety margin and low speed risk. A speed margin close to zero or a negative value indicates that the operating speed has approached or exceeded the physical allowable range, with extremely high speed risk. The physical meaning of interval margin is similar. A positive and large interval margin indicates that the tracking interval has sufficient margin and less pressure on traffic capacity. An interval margin close to zero or a negative value indicates that the tracking interval has reached or exceeded the physical limit, with significant traffic capacity risk.

[0054] A nonlinear mapping method is employed to process the speed margin and interval margin, transforming the margin space into a risk tension space. For the speed margin, the system constructs a formula for calculating the speed tension component, using the inverse proportional function form Kv / (U_speed + Yv), where U_speed represents the speed margin, Kv represents the speed tension coefficient, and Yv represents the speed translation parameter. The speed tension parameter Kv is set to 100 based on industry statistics; this coefficient determines the dimensions and numerical range of the tension component. The speed translation parameter Yv is set to 10 km / h. This parameter is introduced to prevent the inverse proportional function from approaching infinity when the speed margin is close to zero, while simultaneously optimizing the risk sensitivity of the speed margin near 10 km / h. Through this inverse proportional function mapping, when the speed margin decreases from 60 km / h to 10 km / h, the speed tension component increases from 100 / (60+10)=1.43 to 100 / (10+10)=5.00, an increase of 3.5 times; while when the speed margin further decreases from 10 km / h to 2 km / h, the speed tension component increases sharply from 5.00 to 100 / (2+10)=8.33, an increase of 1.67 times, perfectly depicting the nonlinear law of accelerated risk increase when the margin approaches zero.

[0055] For the interval margin, a formula for calculating the interval tension component is also constructed, using the inverse proportional function form of Kh / (Hinterval + Yh), where Kh represents the interval tension coefficient, Hinterval represents the interval margin, and Yh represents the interval shift parameter. The interval shift parameter Yh is set to 500, a value determined based on the typical order of magnitude of the tracking interval, ensuring that the interval tension component and the velocity tension component are within a comparable numerical range. The interval shift parameter Yh is set to 30 seconds. The physical meaning of this parameter is that 30 seconds serves as a risk-sensitive critical point for the tracking interval margin; when the interval margin is less than 30 seconds, the risk increases significantly. Through inverse proportional function mapping, when the interval margin decreases from 120 seconds to 30 seconds, the interval tension component increases from 500 / (120+30)=3.33 to 500 / (30+30)=8.33, an increase of 2.5 times; when the interval margin further decreases to 10 seconds, the interval tension component increases sharply to 500 / (10+30)=12.50, accurately reflecting the nonlinear amplification effect of the tracking interval pressure in the critical region.

[0056] The calculated velocity tension component and interval tension component are weighted and summed to obtain the dynamic margin tension index. Td = w1 × T 速度 +w2×T 间隔 T 速度 For the velocity tension component, T 间隔 The index represents the interval tension component, with w1 as the speed weight and w2 as the interval weight. Based on the relative importance of speed safety and capacity in urban rail transit operation practice, the speed weight w1 is set to 0.55, and the interval weight w2 is set to 0.45, with the weight sum being 1 to ensure the normalization characteristic of the index. This weighting method reflects the priority principle that speed safety is slightly higher than tracking interval efficiency, because exceeding speed limits may directly lead to insufficient braking distance and collisions, while tracking interval pressure mainly affects operational efficiency and passenger flow evacuation capacity. The dynamic margin tension index comprehensively reflects the degree to which the current operational target approaches the physical boundary. The larger the index value, the greater the operational pressure, the smaller the safety margin, and the higher the risk tension. The dynamic attribute of this index is that it changes in real time with the adjustment of operational efficiency parameters, and can sensitively capture the impact of operational plan optimization on the risk level.

[0057] After calculating the dynamic margin tension index, the braking system design availability parameter is selected from the vehicle's basic parameters. This parameter is entered by the operator based on the braking system reliability data provided in the vehicle's technical specifications. Braking system design availability is typically expressed as a percentage, reflecting the probability that the braking system will be in a normal and usable state within its design life cycle, with a typical range of 95% to 99.9%. A higher braking system design availability indicates better system reliability, lower failure risk, and better health; conversely, a lower braking system design availability indicates inherent reliability defects or insufficient redundancy, resulting in a higher health risk.

[0058] The health risk score is obtained by mapping the braking system design availability parameters through the first mapping function. The first mapping function characterizes the correspondence between the train's inherent dynamics and braking deceleration characteristics, and its mathematical form is a piecewise linear function or an exponential decay function. The built-in first mapping function uses R... 健康 =a1×e (-b1)×A可用度 The +c1 exponential mapping form, where A (availability) represents the percentage of braking system design availability, and a1, b1, and c1 are mapping coefficients. Based on historical project data fitting, a1 is set to 95, b1 to 0.05, and c1 to 5, so that when availability is 99.9%, the health risk score is approximately 95 × e. (-0.05)×99.9 +5=5.7, and the health risk score is approximately 95×e when availability is 95%. (-0.05)×95 +5=12.1, and the health risk score is approximately 95×e when availability is 90%. (-0.05)×90 +5 = 18.5. This index mapping accurately depicts the accelerating upward trend of health risk as availability declines. When availability drops from 99% to 95%, the risk increase is relatively gradual, while from 95% to 90%, the risk increase accelerates significantly, consistent with the non-linear relationship between failure rate and availability in reliability engineering. The physical meaning of the first mapping function lies in converting the abstract availability probability into a risk score that can be directly compared with other risk factors, achieving a unified measurement of parameters with different dimensions.

[0059] The absolute value of the maximum design gradient and the reciprocal of the minimum design curve radius are selected from the track parameters. These two parameters are key indicators characterizing the geometric complexity of the track. The absolute value of the maximum design gradient reflects the undulation of the track's longitudinal profile. A steeper gradient means the train needs to overcome a greater component of gravity when going uphill, resulting in a longer braking distance, while also increasing the risk of runaway when going downhill. The reciprocal of the minimum design curve radius reflects the sharpness of the track's horizontal curves. A smaller curve radius results in greater centrifugal force, increased wheel-rail lateral force, limited adhesion utilization, and insufficient superelevation, which may lead to a risk of train derailment. The absolute value of the maximum design gradient is denoted as G. 坡度 The unit is %, and the reciprocal of the minimum design curve radius is denoted as 1 / R.曲线 The unit is m -1 .

[0060] The risk score of the railway line is obtained by mapping the absolute value of the maximum design gradient to the reciprocal of the minimum design curve radius using a second mapping function. This second mapping function characterizes the relationship between the additional resistance acceleration caused by the spatial geometry of the railway line, and its mathematical form is a two-parameter weighted summation function. The built-in second mapping function uses R... 线路 =a2×G 坡度 +b2×1000 / R 曲线 The linear weighted form of +c2, where a1, b2, and c2 are mapping functions, is used. Based on the calculation formulas for gradient resistance and curve resistance in urban rail transit design specifications, a2 is set to 0.25, making the risk contribution value for a 35‰ gradient 0.25 × 35 = 8.75. b2 is set to 0.015, making the risk contribution value for a 300-meter curve radius 0.015 × 1000 / 300 = 0.05, while the risk contribution value for a 150-meter sharp bend is 0.015 × 1000 / 150 = 0.10. The risk contribution increases significantly as the curve radius decreases. c2 is set to 5 as the baseline risk value, ensuring minimal inherent risk even on straight tracks. The physical meaning of the second mapping function is to convert the track geometry parameters into equivalent additional braking requirements. The greater the gradient or the sharper the curve, the greater the braking margin the train needs to reserve, and the higher the corresponding track risk score. This mapping method ensures the quantitative contribution of line complexity to the comprehensive risk tension index, avoiding the problem in traditional methods where line parameters are only used as qualitative references and cannot be quantitatively evaluated.

[0061] The design wheel-rail adhesion coefficient was selected from the environmental parameters. This parameter was determined based on the operating environment conditions during data entry. The design wheel-rail adhesion coefficient directly determines the maximum tangential force that the wheel-rail contact surface can transmit, and is the physical upper limit restricting the train's traction and braking performance. The lower the adhesion coefficient, the wetter or more polluted the rail surface, the smaller the actual braking force that the train can apply, the longer the braking distance, and the higher the environmental risk. A decrease in adhesion coefficient from 0.30 in dry conditions to 0.20 in wet conditions results in a braking capacity reduction of approximately 33%; a further reduction to 0.12 in icy and snowy conditions results in a braking capacity reduction of up to 60%, demonstrating a non-linear amplification effect of environmental severity on safety.

[0062] An environmental risk score is obtained by mapping the design wheel-rail adhesion coefficient value to a third mapping function. This third mapping function characterizes the relationship between the maximum available adhesion limit or adhesion degradation of the wheel and rail caused by external environmental factors such as rain, snow, icing, and tunnel humidity; its mathematical form is an inverse proportional function or a negative exponential function. The system's built-in third mapping function uses R... 环境 =a3 / (U 黏着+Yu)+c3 inverse proportional mapping form, where U 黏着 To design the wheel-rail adhesion coefficient, a3 represents the environmental tension coefficient, Yu represents the adhesion translation parameter, and c3 represents the baseline risk value. Setting a3 to 6, Yu to 0.10, and c3 to 5 results in an environmental risk score of 6 / (0.30+0.10)+5=20 when the adhesion coefficient is 0.30, and 6 / (0.20+0.10)+5=25 when the adhesion coefficient is 0.20. This inverse mapping accurately depicts the nonlinear upward trend of environmental risk as the adhesion coefficient decreases; when the adhesion coefficient decreases from 0.30 to 0.20, the risk increases by 25%, consistent with the physical law of rapidly narrowing safety margins under extreme environmental conditions. The introduction of the third mapping function quantifies the contribution of environmental factors to the overall risk, compensating for the deficiency in traditional GEBR calculations where environmental parameters are only used as braking deceleration corrections and not incorporated into the risk assessment system.

[0063] The calculated health risk score, line risk score, and environmental risk score are weighted and summed to obtain the static inherent tension index. The weighted summation formula is: Tj = w³ × R 健康 +w4×R 线路 +w5×R 环境 In this index, w3 represents health weight, w4 represents line weight, and w5 represents environmental weight. Based on the importance ranking of factors affecting urban rail transit safety, w3 is set to 0.35, w2 to 0.30, and w3 to 0.35, with a total weight of 1. This weight allocation reflects the risk management principle that equipment health and environmental adaptability are given the highest priority, followed by line conditions. The static inherent tension index comprehensively reflects the inherent constraints of the train itself, line infrastructure, and external environment on system safety. Its static attribute lies in the fact that it depends only on physical attribute parameters and is not affected by operational plan adjustments, representing the system's inherent risk baseline. A higher health risk score indicates insufficient braking system reliability; a higher line risk score indicates complex line conditions; and a higher environmental risk score indicates a harsh external environment. A significant increase in any of these three components will raise the static inherent tension index, prompting the system to classify the operating condition as abnormal.

[0064] The comprehensive risk tension index is obtained by weighted summation of the dynamic margin tension index and the static inherent tension index. The weighted summation formula is: Tz = w 动态 ×Td+w 静态 ×Tj, where w 动态 For dynamic weights, w 静态 As static weights, the dynamic weights w 动态 Set to 0.60, static weight w 静态The weighting is set to 0.40, with a weighting sum of 1. This weighting allocation reflects the dominant role of dynamic operational margin in overall risk, as dynamic margin directly reflects the degree to which the current operational plan approaches the physical boundary and is a direct factor triggering risk. While static inherent characteristics determine the risk baseline, static risks can be buffered by sufficient dynamic margin as long as the operational plan is set reasonably. The calculation of the comprehensive risk tension index completes the aggregation transformation from multi-dimensional parameters to a single risk measure. The index value typically ranges from 0 to 100; the higher the value, the higher the comprehensive risk tension, and the closer the system operation is to the critical state of safety or efficiency. When the comprehensive risk tension index exceeds the preset risk threshold, it indicates that the parameter combination as a whole presents high risk pressure, requiring identification as an abnormal operating condition and the adoption of a conservative calculation strategy. When the comprehensive risk tension index is within the safe range, it indicates that the parameter combination risk is controllable, and it can be identified as a normal operating condition, enabling the activation of efficiency optimization strategies.

[0065] For example, let's take the calculation of the comprehensive risk tension index of Metro Line 3 in a certain city under different parameter combinations as an example for detailed explanation. Scenario 1 is a normal operation simulation under the standard design conditions of this line. Step S101 inputs the following parameters: Vehicle basic parameters: braking system design availability 98.5%, 6-car A-type train, rated emergency braking deceleration 1.0 m / s²; Line parameters: design maximum gradient absolute value 30‰, design minimum curve radius 350 meters; Environmental parameters: design wheel-rail adhesion coefficient 0.28 corresponding to normal slightly wet conditions; Operational efficiency parameters: target operating speed 80 km / h, target tracking interval 110 seconds. Speed ​​margin 48 km / h, interval margin 52 seconds. Entering the comprehensive risk tension index calculation, the speed margin of 48 km / h is processed using an inverse proportional function, and the speed tension component T... 速度 =100 / (48+10)=1.724, the interval margin of 52 seconds is processed using an inverse proportional function, and the interval tension component T 间隔 =500 / (52+30)=6.098. Dynamic margin tension index Td=0.55×1.724+0.45×6.098=3.692. The braking system design availability is extracted as 98.5% from the vehicle's basic parameters. The health risk score R is calculated using the first mapping function. 健康 =95×e (-0.05×98.5) +5=5.709. The absolute value of the maximum design gradient (30‰) and the minimum design curve radius (350 meters) are extracted from the route parameters. The route risk score R is then calculated using the second mapping function. 线路 =0.25×30+0.015×1000 / 350+5=12.543. The design wheel-rail adhesion coefficient of 0.28 is extracted from the environmental parameters, and the environmental risk score R is calculated using the third mapping function. 环境=6 / (0.28+0.10)+5=20.789. Static inherent tension index Tj=0.35×5.709+0.30×12.543+0.35×20.789=13.037. Comprehensive risk tension index Tz=0.60×3.692+0.40×13.037=7.430.

[0066] After obtaining the comprehensive risk tension index, it is compared with a preset risk threshold. This preset risk threshold, determined based on historical project data and industry expert experience, represents the quantitative boundary between normal and abnormal operating conditions; therefore, a risk threshold of 65 can be set. When the comprehensive risk tension index is greater than 65, it indicates that although the braking system has no critical faults, adhesion conditions meet requirements, and physical margin is positive, the overall parameter combination exhibits a high-risk tension state. This may indicate multiple overlapping critical constraints, insufficient safety margin, or overly aggressive efficiency targets. In this case, the current simulation condition is determined to be an abnormal operating condition, and subsequent GEBR calculations will adopt a conservative strategy, prioritizing safety over efficiency optimization. When the comprehensive risk tension index is less than or equal to 65, it indicates that the parameter combination is in good condition across multiple dimensions, including physical feasibility, braking margin, line adaptability, and environmental adaptability, and the risk tension is controllable. In this case, the current simulation condition is determined to be a normal operating condition, and subsequent GEBR calculations will employ an efficiency-adaptive strategy, coupling operational tracking intervals for GEBR optimization while ensuring safety.

[0067] S104: If the operating condition is an abnormal operating condition, the vehicle basic parameters, line parameters, environmental parameters and signal safety parameters are input into the preset braking safety model to obtain the safety baseline emergency braking rate.

[0068] In S104 above, after determining the operating condition type, when the parameter combination is identified as belonging to an abnormal operating condition, the vehicle's basic parameters, track parameters, environmental parameters, and signal safety parameters are input into the preset braking safety model to obtain the safety baseline emergency braking rate. The core of abnormal operating conditions is that the operating environment has deviated from the normal design conditions, and there may be multiple adverse factors such as partial failure of the braking system, severe degradation of wheel-rail adhesion, and poor track conditions. At this time, operational efficiency considerations must be completely abandoned, and all computational resources must be focused on determining the braking capacity baseline that can ensure the train can still stop safely under the worst conditions. The purpose of this operating condition separation calculation strategy is to avoid insufficient safety margin or excessive sacrifice of efficiency caused by mixing the same set of calculation logic for normal and abnormal operating conditions. By constructing an independent extreme safety assessment model for abnormal operating conditions, it ensures that even in the most unfavorable scenario with multiple faults superimposed, the train still has reliable emergency braking capability, fundamentally preventing major operational safety accidents such as rear-end collisions and overrunning of track terminals caused by insufficient braking capability.

[0069] The pre-defined braking safety model adopts a multi-level sub-model serial architecture, including four core computational units: a fault degradation correction sub-model, a minimum adhesion constraint sub-model, a train braking dynamics sub-model, and a safety margin superposition sub-model. This hierarchical design follows the physical chain of the train braking process, starting with fault state assessment and gradually superimposing adhesion constraints, dynamic evolution, and safety margin compensation to ultimately form a sufficiently conservative safety baseline emergency braking rate. Parameters are transferred between sub-models through standardized data interfaces, with the output of one sub-model serving as the input constraint for the next, forming a rigorous computational logic chain that ensures that the safety constraints at any stage are accurately reflected in the final value. If the operating condition is an abnormal operating condition, the vehicle's basic parameters, track parameters, environmental parameters, and signal safety parameters are input into the preset braking safety model to obtain the safety baseline emergency braking rate. Specifically, this includes: obtaining the braking fault combination mode from the vehicle's basic parameters and inputting the braking fault combination mode into the fault degradation correction sub-model to calculate the available braking force output ratio correction value; multiplying the available braking force output ratio correction value by the train's rated emergency braking deceleration to obtain the degradation braking deceleration; inputting the wheel-rail adhesion coefficient from the environmental parameters into the minimum adhesion constraint sub-model to calculate the maximum available adhesion limit, and using the maximum available adhesion limit to constrain the amplitude of the degradation braking deceleration, outputting the adhesion constraint emergency braking deceleration; inputting the vehicle's basic parameters, track parameters, environmental parameters, signal safety parameters, and adhesion constraint emergency braking deceleration into the train braking dynamics sub-model to obtain the minimum target deceleration required by the train during the constant braking phase; inputting the minimum target deceleration into the safety margin superposition sub-model to output the safety baseline emergency braking rate.

[0070] Specifically, braking failure combination modes are obtained from the vehicle's basic parameters, which have been identified as one of the key triggering conditions when determining abnormal operating conditions. Braking failure combination modes describe the capability degradation state of the train's braking system under specific failure scenarios. Typical failure modes include complete brake failure of a single bogie, 50% brake unit cutoff, brake failure of a single car, and a decrease in overall vehicle braking force due to brake control unit failure—industry-widely accepted failure scenarios. These failure modes are determined based on the redundancy design principles of urban rail transit vehicle braking systems and the results of Failure Mode and Effects Analysis (FMEA), representing the minimum available capability that the braking system must maintain under single-point or multi-point failure combinations. The selection of braking failure combination modes directly determines the reduction in available braking force in subsequent calculations and is the starting point for assessing ultimate braking capability.

[0071] The acquired braking fault combination patterns are input into the fault degradation correction sub-model, which contains a database of mapping relationships between different fault modes and the proportion of available braking force output. The mathematical core of the fault degradation correction sub-model consists of a set of piecewise functions or lookup tables that output the corresponding correction value for the proportion of available braking force output based on the input fault type code. This correction value is a dimensionless coefficient, typically ranging from 0.5 to 0.8, representing the percentage of the train's actual available braking force under the fault condition relative to the designed rated braking force. For a single bogie failure fault, the system calculates the correction value for the proportion of available braking force output based on the proportion of that bogie to the total braking unit of the train (N...). 总 -N 失效 ) / N 总 , where N 总 N represents the total number of braking units in the entire vehicle. 失效 The number of failed braking units is used. Taking a 6-car A-type train as an example, the entire train has 12 bogies and 24 braking units. If a single bogie failure involves 2 braking units, the available braking force output ratio correction value is (24-2) / 24=0.917. For a 50% braking unit cutoff fault, this fault mode simulates the extreme scenario where a brake control system failure causes half of the braking units to be unresponsive, and the available braking force output ratio correction value is directly set to 0.50. For a single car brake failure, a single car in a 6-car train typically contains 4 braking units, and the available braking force output ratio correction value is (24-4) / 24=0.833. The fault degradation correction sub-model also considers the automatic fault compensation mechanism in the redundant design of the braking system. For models with an emergency backup braking circuit, when a single control circuit fails, the backup circuit can undertake part of the braking task. In this case, the available braking force output ratio correction value is appropriately increased according to the backup circuit capacity, usually by adding a compensation coefficient of 5%-10% to the basic reduction value.

[0072] The calculated available braking force output ratio correction value is multiplied by the train's rated emergency braking deceleration to obtain the degraded braking deceleration. The rated emergency braking deceleration is extracted from the vehicle's basic parameters entered in step S101. This parameter represents the design value of the emergency braking deceleration that the train's braking system can provide under fully functional normal conditions, typically ranging from 1.0 m / s² to 1.2 m / s². The formula for calculating the degraded braking deceleration is a. 降级 =η 故障 ×a 额定 , where a 降级 For degraded braking deceleration, η 故障 a is the correction value for the proportion of available braking force output. 额定 This refers to the rated emergency braking deceleration. The physical meaning of the multiplication operation lies in directly mapping the fault state of the braking system to a proportional decrease in braking deceleration. Assuming that the braking force and deceleration are proportional within a linear range, the reduction in braking force caused by the fault is directly converted into a reduction in deceleration. Taking a subway car in a certain city with a rated emergency braking deceleration of 1.0 m / s² as an example, when a single bogie failure occurs, the available braking force output ratio correction value is 0.917, so the degraded braking deceleration is 1.0 × 0.917 = 0.917 m / s². When a 50% braking unit cutoff fault occurs, the available braking force output ratio correction value is 0.50, so the degraded braking deceleration is 1.0 × 0.50 = 0.50 m / s², the braking capacity decreases by 50%, and the braking distance will increase exponentially. The calculation of degraded braking deceleration completes the transformation from a qualitative description of the fault mode to a quantitative deceleration value, enabling precise quantification of the impact of the braking system fault on the safe braking distance, and providing a corrected braking capacity input for subsequent dynamic simulation.

[0073] The wheel-rail adhesion coefficient from the environmental parameters is input into the minimum adhesion constraint sub-model, which is used to calculate the maximum available adhesion limit that the wheel-rail contact surface can transmit under given adhesion conditions. The wheel-rail adhesion coefficient directly determines the ratio of the maximum tangential force to the normal force that the wheel-rail contact patch can withstand, and is the physical upper limit restricting the traction and braking performance of the train. Based on the classical Coulomb's friction law, the minimum adhesion constraint sub-model constructs the calculation formula for the maximum available adhesion limit, F_adhesion_max = u × m × g, where u represents the design wheel-rail adhesion coefficient, m represents the train mass (extracted from the maximum gross mass AW3 in the vehicle's basic parameters), and g represents the gravitational acceleration. The maximum available adhesion limit is converted into the maximum available adhesion deceleration a_adhesion_max = u × g. This deceleration represents the maximum braking deceleration that the train can theoretically apply under the current adhesion conditions. Exceeding this value will lead to wheel lock-up and slippage, adhesion failure, and loss of braking distance control. For dry conditions with an adhesion coefficient of 0.30, the maximum usable adhesive deceleration is 0.30 × 9.8 = 2.94 m / s², far exceeding the train's rated emergency braking deceleration of 1.0 m / s², indicating that adhesion constraint does not constitute a bottleneck. For wet and slippery conditions with an adhesion coefficient of 0.20, the maximum usable adhesive deceleration drops to 0.20 × 9.8 = 1.96 m / s², still sufficient to support the rated braking deceleration. For severe icy and snowy conditions with an adhesion coefficient of 0.12, the maximum usable adhesive deceleration is only 0.12 × 9.8 = 1.176 m / s², approaching the rated braking deceleration of 1.0 m / s², where adhesion constraint begins to substantially limit braking capacity. If the degraded braking deceleration of 0.50 m / s² due to braking failure is added, adhesion constraint still does not constitute a bottleneck, but the safety margin has been significantly narrowed.

[0074] The maximum available adhesion limit is used to apply amplitude constraints to the deceleration during degraded braking, outputting the adhesion-constrained emergency braking deceleration. The mathematical implementation of the amplitude constraint involves a minimum value operation 'a'. 黏着约束=min(a_degradation, a_adhesion_max), the physical meaning of this operation is that the actual deceleration that the braking system can apply cannot exceed the maximum deceleration that wheel-rail adhesion can transmit; otherwise, wheel lock-up, adhesion failure, and ineffective transmission of braking force to the track will occur. Through this minimum constraint, the system ensures that the adhesion-constrained emergency braking deceleration is always within the physically achievable range, avoiding invalid outputs that exceed physical boundaries. In the aforementioned single bogie failure scenario, the degraded braking deceleration is 0.917 m / s². When the wheel-rail adhesion coefficient is 0.30, the maximum usable adhesion deceleration is 2.94 m / s². The adhesion-constrained emergency braking deceleration is min(0.917, 2.94) = 0.917 m / s², the adhesion constraint is ineffective, and the degraded braking deceleration can be fully achieved. When the wheel-rail adhesion coefficient drops to 0.12, the maximum available adhesive deceleration is 1.176 m / s², and the adhesion-constrained emergency braking deceleration remains min(0.917, 1.176) = 0.917 m / s², indicating that the adhesion constraint does not constitute a bottleneck. However, when the fault mode is 50% brake unit cutoff and the adhesion coefficient is 0.12, the degraded braking deceleration is 0.50 m / s², the maximum available adhesive deceleration is 1.176 m / s², and the adhesion-constrained emergency braking deceleration is min(0.50, 1.176) = 0.50 m / s². In this case, the braking system fault becomes the main bottleneck rather than the adhesion limitation. Through this dual constraint mechanism, the true limiting factors of braking capacity can be automatically identified. Whether it is equipment failure or environmental degradation, it can be accurately reflected in the adhesion-constrained emergency braking deceleration, providing a physically boundary-verified deceleration input for subsequent dynamic simulations.

[0075] Vehicle basic parameters, track parameters, environmental parameters, signal safety parameters, and adhesion-constrained emergency braking deceleration are input into the train braking dynamics sub-model. This sub-model is the core calculation engine of the pre-defined braking safety model, responsible for simulating the entire dynamic evolution of the train from detecting braking demand to complete stopping, and inversely solving for the minimum target deceleration that satisfies the safety protection distance constraint. Based on Newton's second law and kinematic equations, the train braking process is divided into five typical stages: idle stage, traction cut-off stage, coasting stage, braking establishment stage, and constant braking stage. This five-stage division method originates from the IEEE 1474.1 standard and domestic urban rail transit engineering design specifications, accurately reflecting the temporal characteristics of the train from receiving the braking command to the complete establishment of braking force. Each stage has different motion characteristics and control logic, requiring the establishment and solving of corresponding motion equations.

[0076] The physical process of the no-run phase is the time period from when the signaling system issues an emergency braking command to when the Train Onboard Controller (VOBC) receives and begins to respond. During this phase, the train maintains its running state before the braking command was issued. If it is in traction mode, traction force continues to be applied; if it is in coasting mode, it remains coasting. The braking system has not yet intervened. The duration of the no-run phase is extracted from the five-stage braking delay data in the signaling safety parameters. This delay includes the inherent system response time, such as signal transmission delay, VOBC calculation delay, and command issuance delay, with a typical value of 0.5 to 1.0 seconds. The system establishes the motion equations for the no-run phase as u1 = u0 and s1 = u0 × t. 空走 Where u0 represents the initial velocity at the moment of braking triggering, u1 represents the velocity at the end of the idle phase, s1 represents the distance traveled during the idle phase, and t 空走 This represents the duration of the idle travel phase. Since the idle travel phase is extremely short and the train speed is relatively stable, the equation of motion uses a uniform velocity approximation, neglecting the influence of basic resistance. This simplifies the calculation but keeps the error controllable.

[0077] The physical process of the traction cut-off phase is the transition period during which the traction system output is immediately cut off after the VOBC receives the emergency braking command, and the traction force rapidly decays from its current value to zero. During this phase, the train no longer applies traction force, but braking force has not yet been established; the train begins to decelerate under the action of basic resistance. The duration of the traction cut-off phase is also extracted from the five-stage braking delay, typically ranging from 0.3 to 0.5 seconds. This time depends on the turn-off response speed of the traction inverter and the electromagnetic inertia of the traction motor. The equation of motion for the traction cut-off phase is established as u2 = u1 - a. 基本阻力 ×t 牵引切除 s2=u1×t 牵引切除 -1 / 2(a 基本阻力 )×t 2 牵引切除 Where u2 represents the velocity at the end of the traction resection phase, and a 基本阻力 The t represents the equivalent deceleration of the vehicle's basic resistance. 牵引切除 s1 represents the duration of the traction shearing phase, and s2 represents the distance traveled during the traction shearing phase. The basic drag equivalent deceleration is calculated using the Davis formula. 基本阻力 =(A+B×u+C×u 2 ) / m, where A, B, and C are Davis coefficients related to the train's aerodynamic shape, bearing friction, and wheel-rail rolling resistance, extracted from the vehicle's basic parameters or set based on model experience values, u is the instantaneous speed, and m is the train's mass. The speed reduction during the traction cut-off phase is usually small, typically decreasing from 80 km / h to approximately 79.5 km / h, but the existence of this phase increases the braking distance by several meters and must be considered in the calculation of the safety protection distance.

[0078] The physical process of the coasting phase is the transition period after the traction force is completely cut off and the braking command has been issued, but the brake air pressure has not yet been built up to an effective braking force level. During this phase, the train is only subject to basic resistance and additional track resistance; although the braking system has been activated, the braking force output is insufficient to produce a significant deceleration effect. The duration of the coasting phase is extracted from the coasting phase delay in the five-stage braking delay. This delay represents the preparation time for the braking command to be transmitted from the VOBC to the Brake Control Unit (BCU), for the BCU to calculate the braking force distribution scheme, and for the air pressure to begin inflation; a typical value is 0.2 to 0.4 seconds. The motion equation for the coasting phase is established as u3 = u2 - (a 基本阻力 +a 坡度阻力 )×t 惰行 s3=u2×t 惰行 -1 / 2(a 基本阻力 +a 坡度阻力 )×t 2 惰行 Where u3 represents the velocity at the end of the coasting phase, a 坡度阻力 The t represents the equivalent deceleration caused by the additional resistance due to the gradient of the track. 惰行 s3 represents the duration of the coasting phase, and s3 represents the distance traveled during the coasting phase. The equivalent deceleration due to slope resistance is calculated based on the slope angle. 坡度阻力 =g×sin(θ)=g×i, where θ represents the gradient angle and i represents the gradient percentage (extracted from the maximum absolute value of the gradient in the line parameters). Under the small angle approximation, sin(θ)=tan(θ)=i / 1000. For uphill conditions, the gradient resistance is positive, and the train decelerates faster. For downhill conditions, the gradient resistance is negative, which is equivalent to the acceleration added by the gradient. The train decelerates slower or may even accelerate. At this time, the coasting stage becomes the key risk factor for braking distance.

[0079] The physical process of the brake build-up phase is the transition period during which the braking system begins to inflate and build up pressure, and the braking force gradually increases from zero to the design steady-state value. During this phase, the braking force output increases non-linearly with time, and the train deceleration gradually transitions from being primarily driven by resistance to being primarily driven by braking force. The duration of the brake build-up phase is extracted from the brake build-up phase delay in the five-stage braking delay. This delay represents the time from the start of air inflation to pressure stabilization, brake shoe compression of the wheel flange, and the achievement of rated braking force, typically ranging from 0.8 seconds to 1.2 seconds. The equations of motion for the brake build-up phase are established. Considering the engineering simplification assumption of linear growth in braking force, the average deceleration during this phase is denoted as a. 平均 =a 基本阻力 +a 坡度阻力 +1 / 2(a 黏着约束 ), of which 1 / 2 (a 黏着约束 ) indicates that the braking force increases from zero to a 黏着约束 The average contribution during the process. The equation of motion for the braking establishment phase is u4 = u3 - a平均 ×t 建立 s4=u3×t-1 / 2(a 平均 )×t 2 建立 Where u4 represents the speed at the end of the braking setup phase, s4 represents the distance traveled during the braking setup phase, and t 建立 Indicates the duration of the braking setup phase, a 平均 This represents the average deceleration. The brake build-up phase is the stage most significantly affected by the adhesion-constrained emergency braking deceleration among the five phases. The accuracy of this deceleration value directly determines the rate of speed decrease and the distance traveled during the pressure build-up phase.

[0080] The physical process of the constant braking phase is the steady-state braking period from when the brake air pressure is fully established, the braking force is stably output, until the train comes to a complete stop. During this phase, the braking force remains at the designed steady-state value, and the train decelerates continuously to zero speed at an approximately constant rate. The duration of the constant braking phase depends on the remaining speed at the end of the braking establishment phase and the deceleration value during the constant braking phase, requiring inverse kinematic equation solving. The system establishes the kinematic equation for the constant braking phase as u5=0, u5 2 -u4 2 =-2×a 恒定 ×s5, where u5 represents the final stopping speed, a 恒定 Let s represent the target deceleration during the constant braking phase, and s5 represent the distance traveled during the constant braking phase. Through transformation, we obtain s5 = u4. 2 / 2×a 恒定 deceleration a during constant braking phase 恒定 It consists of three parts: basic resistance, gradient resistance, and constant braking deceleration provided by the braking system, expressed as a. 恒定 =a 基本阻力 +a 坡度阻力 +a 目标 , where the minimum target deceleration required by target train a during the constant braking phase is the core output of the final solution of the train braking dynamics sub-model.

[0081] Using the minimum permissible safe protection distance as a constraint, the minimum target deceleration required for the train during the constant braking phase is solved in reverse. The minimum permissible safe protection distance is extracted from signal safety parameters or calculated according to the IEEE 1474.1 standard. This distance represents the minimum safe separation that must be maintained from the train's current position to a potential danger point ahead (such as the tail of the preceding train, the end of the track, or a temporary speed limit point). A typical value is the emergency braking distance at the current speed plus a safety margin. The total braking distance constraint equation s1+s2+s3+s4+s5≤s is then established. 安全防护 , where s 安全防护 This represents the minimum permissible safe protection distance. Substituting the expressions for the travel distance at each stage, we obtain the result containing a.目标 The inequality constraints are used. Through numerical iteration or analytical solution, the system calculates 'a' that makes the total braking distance exactly equal to the safety protection distance. 目标 The numerical value represents the minimum target deceleration required by the train during the constant braking phase. The physical meaning of the minimum target deceleration is the lowest deceleration level the train must achieve during the constant braking phase to stop within a safe distance, given conditions such as system response delay, adhesion-constrained emergency braking deceleration, track gradient, and environmental resistance. A larger deceleration value indicates a more challenging braking task and a stronger braking system capability; a smaller value indicates a more sufficient braking margin and higher safety. The train braking dynamics sub-model, through five-stage refined modeling, accurately captures the time delay losses and dynamic characteristics of each stage of the braking process, avoiding the calculation errors caused by traditional simplified models that treat the braking process as a single uniform deceleration motion. This makes the solution for the minimum target deceleration closer to the physical reality of the actual braking process.

[0082] The calculated minimum target deceleration is input into the safety margin superposition sub-model. This sub-model superimposes additional safety margin compensation on top of the minimum target deceleration, ultimately outputting the emergency braking rate as the safety baseline. The purpose of the safety margin superposition design is to address uncertainties in real-world engineering that cannot be fully modeled, such as simplification errors in the calculation model, uncertainties in parameter measurement, aging and degradation of equipment performance, and random disturbances under extreme conditions. By reserving sufficient safety margins, it ensures that the vehicle still has reliable braking capability even in unexpected scenarios outside the model's prediction range. The safety margin superposition sub-model extracts three key compensation quantities from signal safety parameters: safety margin coefficient, speed and distance measurement error compensation, and system response delay error.

[0083] The safety margin factor is a dimensionless proportionality coefficient, typically ranging from 1.05 to 1.15, representing an additional 5% to 15% margin reserve on top of the minimum target deceleration. This factor comprehensively considers factors such as brake system performance degradation, brake disc and pad wear, brake air leakage, and decreased brake shoe friction coefficient that may occur during long-term operation, ensuring that the train meets braking performance requirements throughout its entire lifespan. Multiplying the minimum target deceleration by the safety margin factor yields the proportionally amplified primary safety baseline deceleration 'a'. 初级目标 =k 安全 ×a 目标 , where k 安全This is the safety margin coefficient. Taking a safety margin coefficient of 1.10 as an example, if the minimum target deceleration is 0.85 m / s², then the primary safety baseline deceleration is 1.10 × 0.85 = 0.935 m / s², which is 10% higher than the minimum target deceleration. This proportional amplification shortens the braking distance by about 9%, providing sufficient buffer space for equipment performance fluctuations.

[0084] The speed and distance measurement error compensation is used to compensate for the inherent measurement errors of the train speed measurement system and the positioning system. Since braking calculations rely on accurate speed and position information, measurement errors directly affect the timing of braking commands and the accuracy of braking curve calculations. The speed and distance measurement error compensation is extracted from signal safety parameters, which are determined comprehensively based on equipment technical specifications such as speed sensor accuracy, track circuit positioning accuracy, and transponder positioning accuracy. Typical values ​​are ±2 km / h for speed error and ±5 meters for distance error. The system converts the speed and distance measurement errors into an equivalent deceleration compensation, calculated using the formula Δa. 测量 =u 误差 / t 响应总 +2×s 误差 / t 响应总 2 , where u 误差 Indicates the speed measurement error, s 误差 t represents the distance measurement error. 响应总 This represents the total delay across the five braking stages. The physical meaning of this formula is that an overestimation of speed will result in the actual speed being higher than the system's perceived speed, requiring additional deceleration compensation; similarly, an overestimation of distance will result in the actual braking distance being shorter than the system's perceived distance, also requiring additional deceleration compensation. Taking a speed error of 2 km / h, a distance error of 5 meters, and a total response delay of 2.5 seconds as an example, the equivalent compensation for speed and distance measurement errors is approximately 0.556 / 2.5 + 2 × 5 / 2.5² = 1.822 m / s². This compensation is relatively large, reflecting the significant impact of measurement uncertainty on braking safety. The system adds the primary safety baseline deceleration to the speed and distance measurement error compensation to obtain the intermediate safety baseline deceleration 'a' after measurement error correction. 中级安全 =a 初级安全 +Δa 测量 .

[0085] The response delay error compensates for the random fluctuations and aging drift inherent in the braking five-stage delay parameters themselves. Although the nominal value of the braking five-stage delay is provided at input, in actual operation, signal transmission may experience momentary congestion, VOBC calculation may be affected by computational load, and the brake air circuit response may fluctuate due to temperature changes, resulting in an actual delay greater than the nominal delay. The system response delay error is extracted from the signal safety parameters, with a typical value of 5% to 10% of the nominal delay. The response delay error is converted into an equivalent deceleration compensation amount, using a simplified calculation method Δa. 延时 =u0×ε 延时 / t 响应总 , where ε 延时 The response delay error ratio is used as an example. Taking an initial speed of 80 km / h (22.22 m / s), a delay error ratio of 10%, and a total response delay of 2.5 seconds, the equivalent compensation for the delay error is approximately 22.22 × 0.10 / 2.5 = 0.889 m / s². This compensation further improves the safety baseline deceleration. Adding the intermediate safety baseline deceleration to the system response delay error compensation yields the final safety baseline emergency braking rate. GEBR 安全底线 =a 中级安全 +Δa 延时 .

[0086] The calculation of the safety baseline emergency braking ratio completes the entire chain of transformation from vehicle physical parameters, track environmental conditions, signal system constraints to the final GEBR value. This value comprehensively considers braking system fault degradation, wheel-rail adhesion limitations, five-stage braking dynamics evolution, safety protection distance constraints, and multiple safety margin compensations, representing the minimum braking performance baseline that vehicles must achieve under abnormal operating conditions. The value of the safety baseline emergency braking ratio is usually significantly higher than the recommended GEBR value under normal operating conditions. This difference reflects the design philosophy of prioritizing safety above all else and sacrificing operational efficiency under abnormal operating conditions. This value will serve as the core input parameter for calculating the signal system ATP overspeed protection curve, determining the movement authorization endpoint, and setting the emergency braking trigger threshold, directly determining the safety protection level of vehicles under extreme conditions.

[0087] For example, the calculation process is illustrated using the abnormal operating conditions of Metro Line 3 in a certain city. The parameters entered for this line include: 50% brake unit isolation fault, maximum gross weight of 310 tons for a 6-car A-type train, rated emergency braking deceleration of 1.0 m / s², total braking delay of 2.5 seconds for the five stages; maximum downhill slope of 35‰; wheel-rail adhesion coefficient of 0.15 under severe icy and snowy conditions; safety margin coefficient of 1.10; speed measurement error ±2 km / h; distance measurement error ±5 meters; delay error ratio of 10%; and safety protection distance of 400 meters. The parameters are determined to simultaneously meet the three triggering conditions of brake failure, low adhesion, and steep gradient. Alternatively, the above parameters can be calculated to obtain a comprehensive risk tension index. When the comprehensive risk tension index exceeds the preset risk threshold, it is considered an abnormal operating condition.

[0088] The preset braking safety model calculation is initiated. The fault degradation correction submodule identifies 50% of the braking units as faulty and outputs a corrected value of 0.50 for the proportion of available braking force. The degraded braking deceleration is calculated to be 0.50 × 1.0 = 0.50 m / s². The minimum adhesion constraint submodule calculates the maximum available adhesion deceleration to be 0.15 × 9.8 = 1.47 m / s². The adhesion constraint emergency braking deceleration is taken as min(0.50, 1.47) = 0.50 m / s², indicating that the braking system fault is the main bottleneck.

[0089] The train braking dynamics submodule establishes a five-stage motion equation. A 35‰ downhill slope generates a gradient resistance of -0.343 m / s², causing the train to increase speed rather than decrease during the first four stages (idle travel, traction cut-off, coasting, and brake establishment), accumulating a distance of 55.86 meters before its speed rises from 80 km / h to 80.89 km / h. The remaining distance for the constant braking stage is 344.14 meters; the minimum target deceleration 'a' is obtained by inverse calculation. 目标 =22.47 2 / (2×344.14)+0.293=1.027m / s², which exceeds the adhesion constraint emergency braking deceleration of 0.50m / s², indicating that the train cannot stop within a safe distance.

[0090] The safety margin superposition submodule adds a safety margin coefficient (1.10 × 1.027 = 1.130 m / s²) to the minimum target deceleration, then adds a speed and distance measurement error compensation of 1.822 m / s² to obtain 2.952 m / s², and finally adds a delay error compensation of 0.889 m / s², ultimately outputting a safety baseline emergency braking rate of 3.841 m / s². This value far exceeds the vehicle's rated braking capacity, prompting a warning in the report that "under current operating conditions, the following vehicles cannot stop safely and are strictly prohibited from operation. It is recommended to limit the speed to below 40 km / h or prohibit operation in icy or snowy weather," providing clear technical basis for engineering decisions.

[0091] S105: If the operating condition is normal operating condition, input the vehicle basic parameters, line parameters, environmental parameters and signal safety parameters into the preset braking safety model to obtain the initial emergency braking rate.

[0092] In S105 above, for the GEBR value under normal operating conditions, the preset braking safety model adopts a hierarchical and progressive calculation architecture. From basic braking capacity assessment, adhesion constraint verification, dynamic full-process simulation to safety margin superposition, it progressively corrects and outputs an initial GEBR value that meets engineering safety requirements. The entire calculation process strictly follows the IEEE 1474.1 international standard and domestic urban rail transit engineering design specifications to ensure the authority and engineering applicability of the values.

[0093] If the operating condition is normal operation, the vehicle's basic parameters, track parameters, environmental parameters, and signal safety parameters are input into the preset braking safety model to obtain the initial emergency braking rate. Specifically, this includes: inputting the normal fault-free mode from the vehicle's basic parameters into the fault degradation correction sub-model, determining the available braking force output ratio correction value as a standard lossless coefficient, and multiplying the standard lossless coefficient by the train's rated emergency braking deceleration to output the standard emergency braking deceleration; inputting the wheel-rail adhesion coefficient under normal conditions from the environmental parameters into the minimum adhesion constraint sub-model, calculating the normal adhesion limit, and using the normal adhesion limit to constrain the amplitude of the standard emergency braking deceleration to output the standard adhesion constraint emergency braking deceleration; inputting the vehicle's basic parameters, track parameters, environmental parameters, signal safety parameters, and the standard adhesion constraint emergency braking deceleration into the train braking dynamics sub-model to obtain the standard target deceleration required by the train under normal operating conditions; and inputting the standard target deceleration into the safety margin superposition sub-model to output the initial emergency braking rate.

[0094] Specifically, after determining that the operating condition is normal, the fault degradation correction sub-model first receives the braking system operating status identifier from the vehicle's basic parameters. The core characteristic of normal operation is that the train's braking system is in full functional condition, with all braking units, bogie braking devices, air brake lines, and electric braking systems operating normally, and no degradation or fault modes present. This sub-model incorporates a standard fault determination logic tree, identifying the value of the "brake fault combination mode" field in the input parameters to determine the current system state.

[0095] For the normal, fault-free mode, this field is marked as "NORMAL MODE" or has a value of "0", indicating that 100% braking capacity is available. Based on this, the model directly outputs a correction value of 1.0 for the available braking force output ratio, which is the standard lossless coefficient. The technical logic behind this setting is that under normal operating conditions, there are no degraded situations such as brake unit disconnection or bogie failure; the actual available emergency braking deceleration of the train should be exactly equal to the rated emergency braking deceleration in the vehicle's design nameplate parameters. This standard lossless coefficient is then multiplied by the rated emergency braking deceleration (typical values ​​such as 1.0 m / s² for Type A cars and 1.2 m / s² for Type B cars) entered in the vehicle's basic parameters.

[0096] The essence of multiplication is to convert the vehicle's factory design capability into a benchmark value of actual usable braking capability under current operating conditions. The output is the standard emergency braking deceleration. This value represents the maximum deceleration capability that the braking system can provide under ideal straight track conditions and without external constraints, and it serves as the benchmark parameter for all subsequent calculations. The architecture design, which employs multiplication correction rather than direct assignment, gives the model good scalability—when it is necessary to analyze the failure conditions of some braking units, only the correction value needs to be modified by a degradation factor such as 0.5 or 0.67 to reuse the same calculation framework. This is also the forward-looking aspect of this sub-model design.

[0097] After the standard emergency braking deceleration is output, the system immediately enters the minimum adhesion constraint sub-model for physical feasibility verification. The initial design purpose of the minimum adhesion constraint sub-model is to resolve a key engineering contradiction: no matter how powerful the vehicle braking system is, the actual braking force that can be applied to the rail is inevitably subject to the rigid constraint of the adhesion at the wheel-rail contact interface. If the braking system attempts to output braking force exceeding the adhesion limit, it will cause the wheels to lock up and skid, which will not only fail to provide effective deceleration but also cause equipment safety problems such as wheel tread abrasion and rail damage, which is absolutely unacceptable in engineering practice.

[0098] The minimum adhesion constraint sub-model extracts the wheel-rail adhesion coefficient under normal conditions from environmental parameters. This coefficient reflects the maximum friction coefficient between the rail surface and the wheel tread. Normal operating conditions typically correspond to dry rail surface conditions, and the adhesion coefficient typically ranges from 0.25 to 0.33. Taking the conventional operating conditions in urban subway tunnels as an example, the standard value is 0.30. Multiplying this coefficient by the gravitational acceleration constant 9.8 m / s² yields the upper limit of the maximum adhesion deceleration in a physical sense. For example, when the adhesion coefficient is 0.30, the calculated result is 0.30 × 9.8 = 2.94 m / s², which means that under the current wheel-rail contact conditions, the theoretically maximum deceleration that the train can achieve cannot exceed 2.94 m / s², a rigid physical boundary determined by Newton's law of friction. The minimum adhesion constraint sub-model numerically compares the calculated normal adhesion limit (2.94 m / s²) with the previously output standard emergency braking deceleration (assumed to be 1.0 m / s²). The comparison logic adopts the minimum value principle: standard adhesion constraint emergency braking deceleration = min(standard emergency braking deceleration, normal adhesion constraint). In the above example, since 1.0 m / s² is significantly less than 2.94 m / s², the adhesion is sufficient and does not constitute a bottleneck constraint; therefore, the output value remains 1.0 m / s². Verification ensures that all subsequent calculations are based on physically achievable braking capabilities, avoiding a disconnect between theoretical calculations and actual execution. This is crucial for the reliability of the ATP overspeed protection curve of the signal system.

[0099] The standard adhesion-constrained emergency braking deceleration, after adhesion constraint verification, is incorporated into the train braking dynamics sub-model, which is the core computational engine of the entire preset braking safety model. Based on the classical Newton's second law F=ma, the model constructs a set of differential equations for the train's longitudinal motion, comprehensively considering all forces such as train mass, braking force, track gradient resistance, curve resistance, air resistance, and wheel-rail rolling resistance, accurately simulating the entire time-domain dynamic process of the train from triggering the emergency braking command to complete stop.

[0100] Under normal operating conditions, the train braking dynamics sub-model divides the braking process into five industry-standard physical stages: the response stage (the human-machine reaction delay from when the driver or ATP system receives an emergency braking command to when it begins to execute the action), the traction cut-off stage (the electrical response time for cutting off traction power and releasing traction torque), the coasting stage (the braking command has been issued but the braking force has not yet been established, and the train coasts due to inertia), the brake establishment stage (the transition time for the brake cylinder pressure to gradually rise to the rated working pressure), and the constant braking stage (the braking force reaches the design value and stabilizes until the train stops). The time parameters for each stage are precisely extracted from the "five-stage braking delay" sub-item in the signal safety parameters, such as 0.5 seconds for the response stage, 0.3 seconds for traction cut-off, 0.8 seconds for coasting, and 0.9 seconds for brake establishment. These values ​​are all from the ATP system response characteristic test report provided by the signal manufacturer and the equipment technical manual of the vehicle braking system manufacturer.

[0101] The train braking dynamics sub-model establishes independent equations of motion for each stage. In the first four stages (from response to braking establishment), the actual braking force of the train is zero or minimal, mainly controlled by the track gradient and basic resistance. The gradient information extracted from the track parameters is converted into gradient resistance components—negative for downhill (acceleration direction) and positive for uphill (deceleration direction). Air resistance is calculated according to the Davis equation and is proportional to the square of the velocity; wheel-rail rolling resistance is related to the total mass of the train and the first power of the velocity. The model uses a numerical integration method (fourth-order Runge-Kutta algorithm) with a time step of 0.01 seconds to iteratively solve for the train velocity and displacement at the end of each stage.

[0102] Upon entering the constant braking phase, a constant braking force term is introduced into the equations. This braking force is equal to the standard adhesion-constrained emergency braking deceleration multiplied by the total train mass (obtained from the AW3 maximum passenger capacity in the vehicle's basic parameters, for example, 310 tons for a 6-car A-type car). At this point, the train is subjected to the combined effects of braking force, gradient resistance, air resistance, and rolling resistance. The net deceleration is the algebraic sum of all forces divided by the mass. The model continues iterative calculations until the train speed drops to zero, outputting the total braking distance and speed curves for each stage of the entire braking process.

[0103] The key output parameter of the train braking dynamics sub-model is the standard target deceleration, which physically means the average deceleration during the constant braking phase necessary for the train to safely brake from its current operating speed (e.g., design operating speed of 80 km / h) to a stop under given track conditions (gradient, curve), vehicle parameters (mass, drag), and environmental conditions (adhesion, wind resistance). This value is obtained by inverse calculation—given the time delays and displacement losses of the first four stages, and the maximum allowable braking distance of the track (determined by the safety protection distance in the signal safety parameters, typically 400 meters in front of the platform), the deceleration level that must be achieved during the constant braking phase is derived. The calculated result may be 0.92 m / s², slightly lower than the standard adhesion-constrained emergency braking deceleration of 1.0 m / s². This is because when the track is slightly uphill and the train is relatively light, the gradient drag provides additional deceleration contribution, and the braking system does not need to output its full capacity to meet the safe stopping requirements.

[0104] While the standard target deceleration meets theoretical braking requirements, engineering practice must fully consider various uncertainties and systematic errors. This is precisely the core value of the safety margin superposition sub-model. The safety margin superposition sub-model receives the standard target deceleration as a baseline input and extracts three key correction coefficients sequentially from the signal safety parameters. The standard safety margin coefficient is an internationally accepted engineering safety redundancy design parameter. The IEEE 1474.1 standard recommends a value range of 1.05-1.15, while the Chinese urban rail transit design code recommends 1.10 for normal operating conditions. The logic behind setting this coefficient is to cover gradual degradation factors that may occur during the long-term operation of the braking system, such as performance degradation, brake pad wear, and brake cylinder seal aging. The safety margin superposition sub-model multiplies the standard target deceleration of 0.92 m / s² by the margin coefficient 1.10 to obtain the first correction value of 1.012 m / s². This means that a 10% safety margin is reserved in the design, ensuring safe stopping even if the braking capacity slightly decreases.

[0105] The standard speed and distance measurement error compensation is designed to address the inherent measurement accuracy limitations of signal system speed radar, speed sensors, and track circuit axle counting equipment. Onboard speed measurement equipment has a speed measurement error of ±2 km / h, and ground axle counting equipment has a train positioning error of ±5 meters. These errors can accumulate in extreme cases. The model employs an engineering conservative approach, assuming all errors occur simultaneously in the most unfavorable direction (i.e., actual speed is underestimated, and actual distance is overestimated), and derives the required additional deceleration increment through kinematic formulas. For a typical operating condition of 80 km / h and a braking distance of 400 meters, this compensation is approximately 0.15 m / s². Adding this compensation to the first correction value of 1.012 m / s² yields the second correction value of 1.162 m / s².

[0106] The standard system response delay error originates from electrical response characteristics such as data transmission delay within the signal system, CPU processing cycles, and relay action time. Although the aforementioned five-stage delay already includes the main response time, there is still approximately 10% random fluctuation in actual engineering. The safety margin superposition sub-model multiplies the sum of the five-stage delays (e.g., 2.5 seconds) by the delay error ratio of 10%, resulting in an additional delay of 0.25 seconds. During this delay, the train continues to coast at the current speed, incurring additional displacement loss, which needs to be compensated for by increasing the braking rate. Kinematically, the deceleration compensation corresponding to this delay is approximately 0.18 m / s². Adding this compensation to the second correction value of 1.162 m / s², the final output initial emergency braking rate is 1.342 m / s².

[0107] The multi-layered design embodies the core principle of urban rail transit: "ensuring passenger safety above all else." The final GEBR value is not a simple theoretical calculation, but a conservative safety value that comprehensively considers all adverse factors such as system degradation, measurement errors, and response delays. This value will be directly written into the configuration database of the signaling system's ZC area controller and VOBC onboard controller, serving as a core parameter for calculating the ATP overspeed protection curve, ensuring reliable stopping of the train under any foreseeable adverse conditions.

[0108] For example, the calculation process is illustrated using the normal operating conditions of Metro Line 2 in a certain city. The parameters input for this line include: 6-car B-type trainsets, rated emergency braking deceleration of 1.2 m / s², and maximum passenger capacity of 240 tons; the line consists of straight tracks in tunnel sections with a local 3‰ uphill slope; the adhesion coefficient of the normal dry rail surface is 0.30; the total delay of the five stages of the signaling system is 2.3 seconds, the safety protection distance is 420 meters, the safety margin coefficient is 1.10, the speed measurement error is ±2 km / h, the distance measurement error is ±5 meters, and the delay error ratio is 10%. Alternatively, the above parameters can be calculated to obtain a comprehensive risk tension index. When the comprehensive risk tension index is less than the preset risk threshold, the parameter combination is determined to be within the normal operating conditions. The preset braking safety model calculation is then initiated. The fault degradation correction submodule identifies the normal fault-free mode, outputs a usable braking force output ratio correction value of 1.0, and calculates the standard emergency braking deceleration as 1.0 × 1.2 = 1.2 m / s². The minimum adhesion constraint submodule calculates the normal adhesion limit as 0.30 × 9.8 = 2.94 m / s², takes the minimum value min(1.2, 2.94) = 1.2 m / s², and outputs the standard adhesion constraint emergency braking deceleration, indicating that the adhesion is sufficient.

[0109] The train braking dynamics submodule establishes a five-stage motion equation. The train approaches the platform at 80 km / h and triggers emergency braking 420 meters from the stopping point. In the first four stages (response 0.5 seconds, traction cut-off 0.3 seconds, coasting 0.8 seconds, and brake establishment 0.9 seconds), under the influence of a 3‰ uphill slope and basic resistance, the train travels a total of 55.26 meters, and its speed decreases to 21.59 m / s. The remaining distance in the constant braking stage is 364.74 meters. The standard target deceleration 'a' is obtained by inverse calculation. 目标 =21.59 2 / (2×364.74)+0.0294=0.668m / s².

[0110] The safety margin superposition submodule sequentially superimposes corrections on the standard target deceleration. The superimposed safety margin coefficient is 0.668 × 1.10 = 0.735 m / s²; the superimposed speed and distance measurement error compensation (speed overestimated to 82 km / h, distance overestimated to 415 meters) of 0.12 m / s² is 0.855 m / s²; the superimposed delay error compensation (additional 0.23 seconds of coasting) of 0.13 m / s² is 0.13 m / s², ultimately outputting an initial emergency braking rate of 0.985 m / s².

[0111] S106: Using operational efficiency parameters as the constraint target, iteratively optimize the initial emergency braking rate to obtain an efficiency-adapted emergency braking rate.

[0112] In S106 above, for normal operating conditions, although the initial emergency braking rate already meets the minimum braking safety requirements, the value of the initial emergency braking rate may be too conservative, leading to excessively long train braking distances, overly cautious movement authorization calculations, and excessively large safety distances between trains. Ultimately, this results in track following intervals exceeding design targets, reduced operational capacity, and wasted track capacity resources. To address this, an iterative optimization algorithm is used to find the optimal GEBR value that satisfies operational efficiency constraints, achieving a dynamic balance between safety and efficiency, while ensuring braking safety.

[0113] Furthermore, using operational efficiency parameters as constraints, the initial emergency braking rate is iteratively optimized to obtain an efficiency-adaptive emergency braking rate. Specifically, this includes: using the initial emergency braking rate as the current iterative braking rate and setting the braking rate adjustment step size; recalculating and summing the current travel distance of the train at each braking stage based on the current iterative braking rate, vehicle basic parameters, track parameters, and environmental parameters to obtain the current iterative safe braking distance; determining the current iterative safe redundancy distance based on the speed and distance measurement error compensation and preset safety margin coefficient in the signal safety parameters, and adding the current iterative safe braking distance to the current iterative safe redundancy distance to obtain the current iterative minimum safe protection distance; dividing the current iterative minimum safe protection distance by the target operating speed to obtain the current iterative protection travel time; and adding the current iterative protection travel time, the preceding train's stopping time at the platform, and the designed time-space difference to obtain the current... Iterative tracking interval; determine whether the current iterative tracking interval is less than or equal to the target tracking interval in the operational efficiency parameters, and whether the current maximum safe allowable speed is greater than or equal to the target operating speed. The current maximum safe allowable speed is derived from the current iterative braking rate. If the current iterative tracking interval is less than or equal to the target tracking interval in the operational efficiency parameters, and the current maximum safe allowable speed is greater than or equal to the target operating speed, then the current iterative braking rate is determined as the efficiency-adaptive emergency braking rate. If the current iterative tracking interval is greater than the target tracking interval in the operational efficiency parameters, or the current maximum safe allowable speed is less than the target operating speed, then the current iterative braking rate is cumulatively adjusted using the braking rate adjustment step size, and the adjusted braking rate is used as the new current iterative braking rate. The process returns to recalculate the current iterative safe braking distance until the constraint conditions are met, and the corresponding efficiency-adaptive emergency braking rate is output.

[0114] Specifically, the initial emergency braking rate is used as the starting point for iteration, and this value is assigned to the braking rate variable for the current iteration. Taking the subway line 2 of a certain city as an example, the initial emergency braking rate is 0.985 m / s², which becomes the input benchmark for the first round of iteration calculation. The braking rate adjustment step size is set synchronously. This parameter determines the magnitude of the braking rate increment in each iteration, directly affecting the iteration convergence speed and calculation accuracy. The step size setting needs to balance computational efficiency and accuracy requirements. The step size is usually set to a fine level of 0.01-0.02 m / s², ensuring a value accuracy on the order of 0.01 m / s² (meeting the ATP system configuration parameter requirements) while ensuring convergence within 10-20 iterations under normal operating conditions, meeting the design goal of completing the entire simulation in 3-5 minutes.

[0115] At the start of each iteration, the model needs to recalculate the actual braking distance of the train based on the current iteration's braking rate. This calculation process reuses the five-stage simulation framework of the train braking dynamics sub-model, but the input parameters undergo a key change: the braking deceleration in the constant braking stage no longer uses the initial emergency braking rate of 0.985 m / s², but instead uses the current iteration's braking rate (still 0.985 m / s² in the first round, increasing incrementally in subsequent rounds). The model extracts the train's total mass of 240 tons and rated operating speed of 80 km / h (22.22 m / s) from the vehicle's basic parameters, the current section gradient of 3‰ and curve radius information from the track parameters, and the air resistance coefficient and wheel-rail rolling resistance coefficient from the environmental parameters. The braking process is still calculated in five stages. During the response stage (0.5 seconds), the train maintains its initial speed of 22.22 m / s while coasting, and the current travel distance is calculated in five stages. During the response phase (0.5 seconds), the train maintains its initial speed of 22.22 m / s and coasts, covering a distance of 22.22 × 0.5 = 11.11 meters. During the traction cut-off phase (0.3 seconds), only affected by gradient resistance of 0.0294 m / s² and foundation resistance of 0.02 m / s², the speed decreases to 22.22 - (0.0294 + 0.02) × 0.3 = 22.205 m / s, covering a distance of (22.22 + 22.205 / 2) × 0.3 = 6.66 meters. During the coasting phase (0.8 seconds), the train continues to decelerate to 22.17 m / s, covering a distance of 17.76 meters. During the braking establishment phase (0.9 seconds), the braking force gradually increases to 50% of the average braking rate of the current iteration, with a comprehensive deceleration of approximately 0.985×0.5+0.0294=0.522m / s², and the speed drops to 21.70m / s, with a travel distance of 19.73 meters.

[0116] The constant braking phase is solved precisely using kinematic equations. The initial velocity upon entering this phase is known to be u0 = 21.70. Given a target stopping speed of u=0, a braking deceleration of 0.985 m / s² (current iteration braking rate), and gradient resistance of 0.0294 m / s² (added in the same direction), the overall deceleration is a = 0.985 + 0.0294 = 1.0144 m / s². Based on u... 2 =u0 2 Given -2as, solve for s = 21.70. 2 / 2×1.0144=232.2 meters. Sum the distances traveled in the five stages: 11.11+6.66+17.76+19.73+232.2=287.46 meters. This value is the safe braking distance for the current iteration, representing the actual distance the train travels from triggering emergency braking to coming to a complete stop.

[0117] When calculating movement authorization for the signaling system's ATP (Automatic Train Protection) system, it is crucial to consider not only the theoretical braking distance but also various system errors and safety redundancies to create a more conservative safety margin. Speed ​​and distance measurement error compensation and a preset safety margin coefficient are extracted from the signaling safety parameters to construct the current iterative safety redundancy distance. The speed and distance measurement error compensation is quantified as a corresponding distance increment. For example, under the combined effect of a ±2 km / h speed measurement error and a ±5 meter positioning error, an additional safety margin of approximately 20 meters is required. The preset safety margin coefficient, typically set at 1.05-1.08 for normal operating conditions, amplifies the braking distance by a percentage to cover gradual uncertainties such as long-term performance degradation of the braking system, environmental temperature fluctuations, and minor rail surface contamination.

[0118] For example, the speed and distance measurement error compensation is set to 20 meters, and the preset safety margin coefficient is 1.06. The current iteration's safety redundancy distance is calculated as: 287.46 × (1.06 - 1) + 20 = 37.25 meters. This redundancy distance is added to the current iteration's safe braking distance to obtain the current iteration's minimum safe protection distance: 287.46 + 37.25 = 324.71 meters. The physical meaning of this value is: under the constraint of the current iteration's braking rate of 0.985 m / s², considering all system errors and safety margins, the ATP signaling system must ensure that the minimum spatial separation between the following vehicle and the preceding vehicle is not less than 324.71 meters; otherwise, there is a risk of rear-end collision. This is the core safety boundary for the Mobile Authorization (MA) calculation.

[0119] The core evaluation indicator for line operation efficiency is the tracking interval (unit: seconds), defined as the time interval between two trains passing the same point, which directly determines the line's hourly throughput capacity and departure frequency. The minimum safe protection distance in the spatial domain is converted into the tracking interval in the time domain, enabling direct comparison with operation efficiency parameters. The conversion logic is based on the assumption of uniform train speed: the time required for the following train to cross the current iterative minimum safe protection distance at the target operating speed is the core component of the protection travel time.

[0120] The target operating speed of 80 km / h is extracted from the operational efficiency parameters and converted to 22.22 m / s. The current iteration protection travel time is calculated as: 324.71 / 22.22 = 14.62 seconds. This time represents the shortest time required for a following train to catch up with the preceding train at the design speed from its rear. The complete current iteration tracking interval also needs to be superimposed with the preceding train's station dwell time at the platform and the design time-space difference. The preceding train's station dwell time is extracted from the operational efficiency parameters, with a typical value of 30 seconds (including door opening and closing, passenger boarding and alighting, and train start preparation). The design time-space difference is an empirical safety margin in signal system engineering design, covering operational factors such as train start acceleration delay, platform departure confirmation time, and communication transmission delay, and is usually taken as 5-8 seconds; in this case, it is taken as 6 seconds.

[0121] The final calculated tracking interval for the current iteration is: 14.62 + 30 + 6 = 50.62 seconds. The engineering meaning of this value is: under the constraint of the current iterative braking rate of 0.985 m / s², and ensuring the safe braking distance and protection interval, the theoretical minimum departure interval of the line is 50.62 seconds, meaning a maximum of 3300 / 50.62 ≈ 71 trains can depart per hour. This tracking interval value will be compared with the target tracking interval in the operational efficiency parameters to determine whether the current braking rate meets the operational efficiency requirements. In addition to the tracking interval constraint, it is also necessary to verify whether the current iterative braking rate supports safe operation at the target operating speed. This verification is achieved by calculating the current maximum safe permissible speed, which is physically defined as: the maximum initial speed at which the train can safely brake to a stop under the given current iterative braking rate and known braking distance constraints (such as a 420-meter safety protection distance in front of the platform). If this speed is less than the target operating speed of 80 km / h, it indicates that the current braking rate is insufficient to support the design operating speed, posing a risk of overspeed protection failure, and the braking rate must be further increased.

[0122] The reverse derivation uses the inverse solution of the five-stage braking dynamics equation. The known usable braking distance during the constant braking stage is 420 - (11.11 + 6.66 + 17.76 + 19.73) = 364.74 meters (the distance loss in the first four stages is approximately 55.26 meters, consistent with the previous calculation). Given that the final speed of the constant braking phase is 0, and the braking deceleration is the current iteration braking rate of 0.985 m / s² plus the gradient resistance of 0.0294 m / s², the initial speed of this phase is calculated as u0 = √(2 × (0.985 + 0.0294) × 364.74 = 27.20 m / s²). The cumulative speed loss over the first four phases is approximately 0.5 m / s, yielding the maximum permissible speed at the moment the train triggers braking, which is approximately 27.70 m / s, or 99.7 km / h. At this point, the current maximum safe permissible speed of 99.7 km / h is greater than the target operating speed of 80 km / h, indicating that under the constraint of the current iteration braking rate of 0.985 m / s², the train has sufficient braking capacity reserve even when operating at 80 km / h, satisfying the safety constraints. This comparative verification ensures that even if the GEBR value continues to decrease in subsequent iterations (to optimize the tracking interval), it will not threaten basic operational safety. This is the safety boundary guarantee mechanism of the iterative optimization algorithm.

[0123] After calculating the current maximum permissible speed, we simultaneously evaluate whether the current iterative tracking interval of 50.62 seconds and the current maximum permissible speed of 99.7 km / h satisfy the dual constraints. We extract the target tracking interval from the operational efficiency parameters, assuming the line's design requirement is 90 seconds (corresponding to a capacity of 40 trains per hour). The comparison results show that the current iterative tracking interval of 50.62 seconds is significantly smaller than the target tracking interval of 90 seconds, satisfying the efficiency constraint and leaving substantial room for optimization; the current maximum permissible speed of 99.7 km / h is greater than the target operating speed of 80 km / h, thus also satisfying the safety constraint.

[0124] The current iterative braking rate is considered an efficiency-adapted emergency braking rate and the iteration terminates only when both conditions are met simultaneously. In the current situation, although both constraints are satisfied, a significant redundancy of 40 seconds (90-50.62) in the tracking interval is identified. This means the current braking rate of 0.985 m / s² is too conservative. The braking distance can be extended and the tracking interval increased by reducing the braking rate until it approaches the 90-second target value. This reduces the performance requirements of the vehicle's braking system, decreases brake wear, extends maintenance cycles, and lowers the total lifecycle operating cost, while maintaining operational efficiency. However, the iteration direction is set as gradient ascent, i.e., gradually increasing the braking rate to compress the tracking interval. This direction is suitable for scenarios where the initial emergency braking rate is too conservative, resulting in a tracking interval far exceeding the target value. If the initial value in actual operating conditions is close to or exceeds the efficiency constraint (e.g., initial tracking interval 95 seconds > target 90 seconds), then the braking rate needs to be increased to shorten the braking distance and compress the tracking interval. When the initial tracking interval is greater than the target tracking interval, a positive step size (+0.015m / s²) is used to increase the braking rate; when the initial tracking interval is less than the target tracking interval, a negative step size (-0.015m / s²) is used to decrease the braking rate, thus achieving bidirectional optimization.

[0125] When the first iteration fails to meet the convergence condition (in this case, the double constraints are met but optimization space exists), the model adjusts the braking rate of the current iteration using a braking rate adjustment step size. The adjusted value is calculated as: 0.985 + 0.015 = 1.000 m / s², and this new braking rate becomes the input benchmark for the second iteration. The model automatically returns to the step of recalculating the safe braking distance for the current iteration, initiating a new round of five-stage braking simulation. In the second iteration, the overall deceleration during the constant braking stage increases to 1.000 + 0.0294 = 1.0294 m / s², and the braking distance is shortened to 21.70 m / s². 2 / 2×1.0294=228.7 meters, the total braking distance of the five stages is 283.96 meters, the minimum safe protection distance after adding safety redundancy is 320.21 meters, the protection travel time is 14.41 seconds, and the tracking interval is 50.41 seconds. This value is still much smaller than the 90-second target and the dual constraints are satisfied, so continue iterating.

[0126] The iterative process continues, with the braking rate increasing by 0.015 m / s² in each round, and the tracking interval gradually compressed. When iterating to a certain round (assuming the 15th round, the braking rate reaches 1.195 m / s²), the calculated tracking interval first reaches 89.8 seconds, which is less than the target of 90 seconds and close to the critical value. If the braking rate is further increased to 1.210 m / s² at this point, the tracking interval will be compressed to 88.5 seconds. Although it still meets the constraint of being less than 90 seconds, it is already lower than the target value, which does not conform to the optimization principle of "as close as possible to but not exceeding the target value". The iteration stops at the 15th round, and the current iteration braking rate of 1.195 m / s² is determined as the efficiency-adapted emergency protection braking rate. When the iteration convergence condition is met, a termination command is triggered, and the current iteration braking rate is officially marked as the efficiency-adapted emergency protection braking rate and output. This output value is 0.210 m / s² (approximately 21%) higher than the initial emergency protection braking rate of 0.985 m / s², reflecting the optimization of braking capability while ensuring operational efficiency.

[0127] For example, the complete iterative process is illustrated using the normal operating conditions of Metro Line 2 in a certain city. The system receives an initial emergency braking rate of 0.985 m / s² as the starting point for iteration, sets the braking rate adjustment step size to 0.015 m / s², and extracts the target tracking interval (90 seconds), target operating speed (80 km / h), preceding train stop time (30 seconds), and design time-space difference value (6 seconds) from the operational efficiency parameters. First iteration: Current iteration braking rate 0.985 m / s², recalculate the five-stage braking distance: response stage 11.11 meters, traction shearing 6.66 meters, coasting 17.76 meters, brake establishment 19.73 meters, constant braking stage 21.70 meters. 2 / 2×1.0144=232.2 meters, the total of five stages is 287.46 meters, which is the safe braking distance for the current iteration. Speed ​​and distance measurement error compensation is 20 meters, and a safety margin coefficient of 1.06 generates a redundancy of 287.46×0.06=17.25 meters. The current iteration's safe redundancy distance is 37.25 meters, and the minimum safe protection distance is 287.46+37.25=324.71 meters. The protection travel time is 324.71 / 22.22=14.62 seconds, and the current iteration's tracking interval is 14.62+30+6=50.62 seconds. The maximum safe permissible speed is approximately 99.7 km / h. Judgment: Tracking interval 50.62 seconds << 90 seconds, speed 99.7 km / h > 80 km / h. Both constraints are satisfied, but the tracking interval redundancy is too large, requiring further optimization. The braking rate is adjusted by step size: 0.985 + 0.015 = 1.000 m / s², entering the second iteration. The braking distance is shortened to 283.96 meters, the protection distance is 320.21 meters, and the tracking interval is 50.41 seconds, requiring further iterations. The model automatically performs the third (1.015 m / s²) and fourth (1.030 m / s²) round-by-round calculations.

[0128] When iterating to the 14th round, the current iteration braking rate is 0.985 + 0.015 × 13 = 1.180 m / s², and the calculated tracking interval is 90.5 seconds, exceeding the 90-second target for the first time and failing to meet the efficiency constraint. Regressing to the 13th round, the braking rate is 1.165 m / s², and the tracking interval is 91.2 seconds, also exceeding the target. Continuing to regress to the 12th round, the braking rate is 1.150 m / s², and the tracking interval is 91.8 seconds, still exceeding the target. At this point, it is recognized that the iteration step size of 0.015 m / s² is too large, triggering a refined search mode, reducing the step size to 0.005 m / s² for local optimization. Starting from 1.150 m / s², iterate upwards in steps of 0.005 m / s²: 1.155 m / s² corresponds to 91.5 seconds, 1.160 m / s² to 91.1 seconds, 1.165 m / s² to 90.7 seconds, 1.170 m / s² to 90.3 seconds, and 1.175 m / s² to 89.9 seconds. When the braking rate reaches 1.175 m / s², the tracking interval of 89.9 seconds is less than 90 seconds for the first time and is closest to the target value. The maximum safe allowable speed remains 96.5 km / h > 80 km / h, perfectly satisfying the dual constraints. The iteration terminates, and the output efficiency is adapted to the emergency braking rate of 1.175 m / s².

[0129] In one possible implementation, to further optimize operating costs, a bidirectional convergent iterative strategy can be adopted. The goal is not only to meet efficiency constraints but also to ensure that the actual tracking interval converges precisely to the target tracking interval. Let's take an example with an initial tracking interval of 50.62 seconds and a target tracking interval of 90 seconds. If the current iterative tracking interval (50.62 seconds) is much smaller than the target tracking interval (90 seconds), this indicates that the current initial emergency braking rate (0.985 m / s²) is set too conservatively. While it meets efficiency requirements, excessively frequent and abrupt braking will lead to unnecessary equipment wear and energy consumption. Therefore, the iterative algorithm will initiate reverse optimization: since the current iterative tracking interval is smaller than the target tracking interval, the current iterative braking rate will be cumulatively adjusted (i.e., the braking rate will be reduced). For example, using an adjustment step size (e.g., 0.01 m / s²), the braking rate is reduced from 0.985 m / s² to 0.975 m / s². The adjusted 0.975 m / s² is then used as the new current iterative braking rate, and the "current iterative tracking interval" is recalculated. Because the braking rate decreases, the new tracking interval will be extended (e.g., to 52.1 seconds), moving closer to the target value of 90 seconds. This iterative process of "reducing the braking rate and extending the tracking interval" continues. The iteration terminates when the "current iterative tracking interval" first approaches and satisfies (e.g., less than or equal to) the target tracking interval of 90 seconds from below. The optimal balance point is then determined. The braking rate corresponding to the termination of the iteration is determined as the final efficiency-adapted emergency braking rate and output.

[0130] S107: Generate a list of corresponding emergency braking rate values ​​based on the emergency braking rate and efficiency adaptation of the safety baseline emergency braking rate.

[0131] In S107 above, after obtaining the emergency braking rate for safety baseline under abnormal operating conditions and the emergency braking rate for efficiency adaptation under normal operating conditions, the simulation calculation results may be abnormal due to reasons such as incorrect input parameter entry, improper selection of operating condition boundaries, and adaptation deviation of the calculation model. Direct output may lead to engineering design risks. By retrieving GEBR value samples from similar historical projects, using cluster analysis algorithms to construct a reasonable value range, cross-validating the current calculation results, identifying abnormal deviations, and triggering parameter correction processes, the final output list of emergency braking rate values ​​is ensured to have engineering rationality, industry comparability, and historical consistency.

[0132] Based on the safety baseline emergency braking rate and the efficiency adaptation emergency braking rate, a corresponding list of emergency braking rate values ​​is generated. Specifically, this involves: obtaining a historical dataset of similar projects identical to the metro project from a historical project database. This dataset includes historical input parameters, historical safety baseline emergency braking rates, and historical efficiency adaptation emergency braking rates. Clustering analysis algorithms are used to cluster the historical dataset to determine reasonable ranges for both the historical safety baseline braking rate and the historical efficiency adaptation braking rate. If both the safety baseline emergency braking rate and the efficiency adaptation emergency braking rate fall within their respective reasonable ranges, the verification is considered successful, and a corresponding list of emergency braking rate values ​​is generated. If either the safety baseline emergency braking rate or the efficiency adaptation emergency braking rate does not fall within its reasonable range, the verification is considered abnormal, and the basic parameters are recalculated based on the historical dataset until the verification is successful and the corresponding list of emergency braking rate values ​​is output.

[0133] Specifically, the project database stores a large number of GEBR value files for completed metro lines. Each record includes a complete snapshot of input parameters, operating condition classification labels, simulation process logs, the final determined safety baseline emergency braking rate and efficiency adaptation emergency braking rate, as well as feedback data after the project's actual operation. The database establishes a multi-dimensional index according to project attributes, covering key feature labels such as line level (urban express / ordinary metro / light rail), vehicle type (Type A / Type B / Type C), train length (6 cars / 8 cars), maximum operating speed (80km / h / 100km / h / 120km / h), line environment (fully underground / mainly elevated / mainly ground), climate region (temperate / subtropical / cold region), and adhesion conditions (mainly dry / rainy and slippery / frequent snow and ice). A multi-level similarity matching algorithm is employed to extract core feature vectors from the basic parameters of the current metro project. These include vehicle basic parameters such as total mass of the train, train formation type, and braking system configuration; line parameters such as maximum gradient, minimum curve radius, and tunnel ratio; environmental parameters such as wheel-rail adhesion coefficient range and climate characteristics; and operational efficiency parameters such as target tracking interval and target operating speed. These feature parameters constitute a high-dimensional feature space, and the Euclidean distance or cosine similarity between each historical project and the current project in this feature space is calculated using a historical database.

[0134] Taking the aforementioned Metro Line 2 of a certain city as an example, the core features were extracted: 6-car B-type trains, total mass 240 tons, target operating speed 80 km / h, maximum gradient 30‰, wheel-rail adhesion coefficient 0.18 (wet and slippery conditions), all underground lines, and temperate climate. Database retrieval yielded 15 historical projects, including the already operational Line 1 extension in a neighboring city (feature distance 0.12), Phase I of Line 3 in a provincial capital city (feature distance 0.18), and the L1 light rail line in a coastal city (feature distance 0.35). A similarity threshold of feature distance < 0.30 was set, and finally, 10 highly similar historical projects were selected to form a historical similar project dataset. This dataset not only includes historical input parameters (such as a total vehicle mass of 238 tons, target speed of 80 km / h, and maximum gradient of 28‰ for a certain project), but also the corresponding historical safety baseline emergency braking rates (such as 10 values ​​including 0.92 m / s², 0.95 m / s², and 0.89 m / s²) and historical efficiency adaptation emergency braking rates (such as 10 values ​​including 1.18 m / s², 1.21 m / s², and 1.15 m / s²).

[0135] When searching for similar historical projects, environmental matching constraints need to be added. When obtaining similar historical projects, it is necessary to ensure that the environments are similar to avoid environmental differences affecting the accuracy of braking rate values. The environmental matching logic is divided into two dimensions: geographical environment and climate environment. Geographical environment constraints require similar proportions of line types. For example, if the current project is a fully underground line, then the proportion of underground sections in historical projects must be >80%, excluding lines that are mainly elevated or ground-level, because the wheel-rail adhesion characteristics and air resistance features caused by the relatively enclosed environment inside tunnels are significantly different from those in open-air environments. Climate environment constraints require consistent climate zones. If the current project is located in a temperate monsoon climate zone with an annual precipitation of 300-800mm, then historical projects must also be from temperate or subtropical humid zones, excluding projects from arid regions (where adhesion coefficients are consistently high) or cold regions (where winter snow and ice result in extremely low adhesion coefficients).

[0136] For example, in the initial matching of 15 historical projects for Metro Line 2 in a certain city, three projects were identified as originating from the arid Northwest, the cold Northeast, and the hot and humid South China, respectively. Although the vehicle and line parameters were similar, the environmental differences were significant. The Northwest project had an annual precipitation of only 150mm, and the wheel-rail adhesion coefficient remained above 0.25 year-round, far exceeding the current project's wet-slip condition setting of 0.18. The Northeast project experienced winter temperatures as low as -30℃, with frequent icing conditions, and the adhesion coefficient could drop to 0.10, a huge difference from the temperate environment of the current project. The South China project had an average annual temperature of 28℃, and the high temperature caused severe brake disc thermal fade, making its braking characteristics incomparable to those of the temperate project. The system automatically eliminated these three environmentally mismatched projects, ultimately identifying 10 similar historical projects with comparable environments, forming a high-quality dataset of similar historical projects, laying a reliable foundation for subsequent cluster analysis and reasonable interval construction.

[0137] The historical dataset of similar projects contains 10 sets of historical emergency braking rate samples for safety baselines and 10 sets of historical emergency braking rate samples for efficiency adaptations. These values ​​are scattered, and directly calculating the maximum and minimum values ​​as the boundaries of a reasonable range is easily affected by extreme values, resulting in an overly wide range that loses its verification significance. K-means clustering analysis is used to cluster the samples, identify the mainstream distribution areas of the data, remove outliers, and construct a statistically significant reasonable value range. The choice of clustering algorithm is based on the distribution characteristics of the GEBR values: although the parameters of historical projects are similar, due to differences in design units, vehicle manufacturers, and engineering margin considerations, the GEBR values ​​exhibit multi-center clustering characteristics. For example, conservative design units tend to use lower values ​​(safety baseline 0.88-0.92 m / s²), while aggressive units tend to use higher values ​​(0.95-1.00 m / s²), forming obvious clusters.

[0138] The K-means clustering algorithm is implemented independently for both the emergency braking rate for safety baseline and the emergency braking rate for efficiency adaptation. Taking the historical emergency braking rate for safety baseline as an example, 10 sample values ​​{0.89, 0.92, 0.95, 0.91, 0.88, 0.93, 0.90, 0.94, 0.87, 0.96} are input into the clustering model as one-dimensional data points, with a preset cluster size K=2 (based on industry experience, GEBR values ​​typically exhibit two main approaches: conservative and balanced). The algorithm randomly initializes two cluster centers, for example, c1=0.89 and c2=0.95, calculates the distance from each sample point to the two cluster centers, and assigns the sample to the nearest cluster. After multiple iterations, the cluster centers gradually converged to stable positions, ultimately forming two clusters: Cluster 1 = {0.87, 0.88, 0.89, 0.90, 0.91} ​​and Cluster 2 = {0.92, 0.93, 0.94, 0.95, 0.96}. Cluster 1 was identified as the dominant cluster (5 samples, accounting for 50%), and Cluster 2 as the secondary cluster (5 samples, accounting for 50%). Since the sample sizes of the two clusters were similar, the system adopted a merging strategy, combining the two clusters and calculating the overall statistical characteristics. The mean of the 10 merged samples was 0.915 m / s², and the standard deviation was 0.029 m / s². Based on the mean and standard deviation, a reasonable range for the historical safety baseline braking rate was determined [mean - 1.5 times the standard deviation, mean + 1.5 times the standard deviation], effectively eliminating extreme outliers. The calculated reasonable range is [0.915-1.5×0.029, 0.915+1.5×0.029]=[0.871, 0.959]m / s². The physical meaning of this range is: based on historical big data statistics from similar projects, under similar vehicle, route, and environmental conditions, the reasonable value for the emergency braking rate under abnormal extreme operating conditions should fall within the range of 0.871-0.959m / s². Values ​​exceeding this range require special review.

[0139] The historical efficiency-adapted emergency braking rate was analyzed using the same clustering process. Ten historical sample values ​​{1.15, 1.18, 1.21, 1.17, 1.14, 1.19, 1.16, 1.22, 1.13, 1.23} were clustered using K-means, forming cluster 1 = {1.13, 1.14, 1.15, 1.16, 1.17} and cluster 2 = {1.18, 1.19, 1.21, 1.22, 1.23}. The combined braking mean was calculated to be 1.178 m / s², with a standard deviation of 0.034 m / s². The reasonable range for the historical efficiency-adapted braking rate is [1.178 - 1.5 × 0.034, 1.178 + 1.5 × 0.034] = [1.127, 1.229] m / s². This range represents a reasonable range of GEBR values ​​under normal operating conditions, balancing tracking interval efficiency and braking safety, and provides a historical benchmark for the efficiency adaptation and emergency braking rate of the current project.

[0140] After constructing the reasonable range, the system automatically retrieves the calculated emergency braking rate (e.g., 0.985 m / s²) and efficiency-adaptive emergency braking rate (e.g., 1.175 m / s²) for the current subway project and cross-checks them with the historical reasonable range. The check is considered successful only if both calculated results fall within the corresponding historical reasonable range; otherwise, a check anomaly alarm is triggered. Comparing the current project's emergency braking rate of 0.985 m / s² with the historical reasonable range [0.871, 0.959] m / s², it is found that 0.985 > 0.959, exceeding the upper limit of the reasonable range by 0.026 m / s². This deviation indicates that the currently calculated abnormal operating condition GEBR value is significantly higher than the mainstream level of similar historical projects. Possible reasons include: excessively high redundancy configuration of the braking system in the input parameters, overly optimistic adhesion coefficient values, insufficiently stringent selection of fault condition combinations, or an excessively small safety margin coefficient setting in the calculation model, leading to an overly aggressive value. Meanwhile, the efficiency-adapted emergency braking rate of 1.175 m / s² was compared with the reasonable range of historical efficiency-adapted braking rates [1.127, 1.229] m / s². It was found that 1.127 < 1.175 < 1.229, which falls within the reasonable range, and the verification was passed.

[0141] Because the emergency braking rate for safety baseline is not within the reasonable range of historical safety baseline braking rates, the verification is deemed abnormal. The direct generation of an emergency braking rate value list is rejected, and instead, a parameter correction process based on historical datasets of similar projects is initiated. GEBR values ​​should not be calculated in isolation; they must be cross-validated using historical big data to ensure they are within the acceptable range based on industry experience. This avoids engineering risks caused by abnormal parameters or calculation errors in individual projects, a systematic quality control that traditional manual value-taking methods cannot achieve. After the verification anomaly is triggered, it is necessary to locate the root cause parameter that causes the value to deviate from the reasonable range. Deviation characteristic parameter identification uses a multi-dimensional feature distance analysis algorithm. The basic parameters of the current subway project are compared item by item with each historical input parameter in the historical dataset of similar projects, and the feature distance is calculated to identify abnormal parameters that significantly deviate from the historical mean. The feature distance is defined as the standardized deviation di = |pi - pi1| / σi between the current parameter value and the historical mean of that parameter in similar projects, where p1 represents the i-th parameter value of the current project, pi1 represents the historical mean of that parameter, and σi represents the historical standard deviation of that parameter. When di > 2, the parameter is marked as a deviation characteristic parameter.

[0142] Taking the current Metro Line 2 as an example, the system extracts the braking unit configuration parameters from the vehicle's basic parameters: the current project input is "4 sets of electric brakes + 2 sets of air brakes per bogie, with the fault condition selected as single bogie failure," corresponding to an equivalent braking force redundancy coefficient of 1.8. In historical datasets of similar projects, the braking force redundancy coefficients for 10 projects are {1.5, 1.6, 1.5, 1.6, 1.5, 1.6, 1.5, 1.5, 1.6, 1.5}, with a mean p1 = 1.54 and a standard deviation σp = 0.048. The characteristic distance d between the current project's redundancy coefficient of 1.8 and the historical mean is d = |1.8 - 1.54| / 0.048 = 5.42. 5.42 > 2 is identified as a deviation characteristic parameter. This deviation reveals that the current project's braking system configuration is overly redundant. Even if a single bogie fails under abnormal conditions, the remaining braking force still far exceeds the level of historical projects, resulting in a higher calculated emergency braking rate as the safety baseline.

[0143] Further examination of other key parameters revealed the following: The maximum gradient of the track parameters was 30‰, the historical average was 28.5‰, the standard deviation was 2.1‰, and the characteristic distance was 0.71. At this point, 0.71 is less than 2 and does not exceed the threshold. The wheel-rail adhesion coefficient was 0.18, the historical average was 0.175, the standard deviation was 0.015, and the characteristic distance was 0.33. At this point, 0.33 is less than 2 and is normal. The signal wheel-rail adhesion coefficient was also 0.18, the historical average was 0.175, the standard deviation was 0.015, and the characteristic distance was 1.67. 1.67 is less than 2 and is within the normal range. Based on the above, the braking force redundancy coefficient of 1.8 is the only deviation characteristic parameter and becomes the core target for parameter correction. After identifying the deviation characteristic parameter, the corresponding default completion value was retrieved from the built-in knowledge base of the AI / big data analysis module. This knowledge base stores correction rules and recommended parameter values ​​for various typical deviations, built based on successful experience from historical projects and industry design specifications. Regarding the deviation of a high braking redundancy coefficient, the knowledge base records show that the standard braking unit configuration for a 6-car B-type trainset should be "4 sets of electric brakes + 2 sets of air brakes per bogie, and the abnormal operating condition failure mode should select 50% braking unit failure per car (2 bogies)," with a corresponding standard value of 1.55 for the braking redundancy coefficient. This default completed value comes from the specification requirements of the IEEE 1474.1 standard regarding the GEBR value taking conditions, as well as the weighted average configuration of 10 samples from similar historical projects.

[0144] The braking force redundancy coefficient is automatically corrected from the current input of 1.8 to the default completed value of 1.55. Simultaneously, the description of the braking fault combination mode in the vehicle's basic parameters is adjusted from "all bogies failed" to "50% of braking units in a single car failed," ensuring semantic consistency of the parameters. The corrected basic parameters are marked as "AI-assisted correction version," retaining the original input parameters as a comparison reference for subsequent manual review. After correction, the system automatically returns to step S105 to recalculate the corrected vehicle basic parameters, track parameters, environmental parameters, and signal safety parameters. During the recalculation, the fault degradation correction model identifies the new braking fault combination "50% of braking units in a single car failed," meaning that in a 6-car train, 4 out of 8 braking units in 2 bogies of a certain car have failed. The total braking force of the entire train decreases from the original 48 units to 44 units, and the available braking force ratio decreases from 75% (4 bogies failed, 44 / 48 remaining) to 91.7% (4 units failed, 44 / 48 remaining). This ratio is significantly lower than the configuration before correction. Under the constraints of adhesion and mass parameters, the revised available braking force was used to recalculate the five-stage braking process. The actual braking deceleration during the constant braking stage decreased, ultimately yielding a new safety baseline emergency braking rate. After resimulation, the new safety baseline emergency braking rate converged to 0.945 m / s², a decrease of 0.040 m / s² from the previous 0.985 m / s², and is closer to the central value of 0.915 m / s² within the historical reasonable range.

[0145] After completing parameter correction and recalculation, the verification logic of step S107 is executed again to determine whether the corrected safety baseline emergency braking rate of 0.945 m / s² falls within the reasonable range of historical safety baseline braking rates [0.871, 0.959] m / s². The comparison results show that 0.871 < 0.945 < 0.959, and the verification passes. Meanwhile, the efficiency-adaptive emergency braking rate is also affected during parameter correction because the braking force redundancy coefficient is reduced, leading to a tighter braking capacity boundary under normal operating conditions. The iterative optimization process of step S106 is re-executed, eventually converging to 1.165 m / s² (slightly lower than the original 1.175 m / s²), still within the reasonable range of historical efficiency-adaptive braking rates [1.127, 1.229] m / s², and the verification passes. After both verifications pass, the system determines that the corrected GEBR value has historical rationality and engineering reliability, and formally starts the process of generating the emergency braking rate value list. The convergence control mechanism of iterative correction and verification ensures that even if the initial input parameters are abnormal, AI-assisted intelligent correction can automatically revert to a reasonable value range, avoiding repeated manual trial and error. If the verification still fails after multiple rounds of correction (e.g., exceeding the reasonable range for 5 consecutive iterations), a manual intervention alarm is triggered, indicating that there may be special characteristics of the project (e.g., extremely steep slopes, unconventional groupings) or insufficient historical database samples, requiring human expert intervention for review to prevent the AI ​​from getting stuck in an infinite loop.

[0146] After successful verification, the system proceeds to the result output and report generation module. Based on the safety baseline emergency braking rate of 0.945 m / s² and the efficiency adaptation emergency braking rate of 1.165 m / s², a corresponding list of emergency braking rate values ​​is generated. This list uses a standardized table format, categorized by operating condition type, ensuring that the signal system ZC controller and VOBC on-board controller can directly parse and apply it. The core content of the list includes three main sections: recommended GEBR values ​​for operating condition classification, associated technical parameters, and linkage switching thresholds. The recommended GEBR values ​​for operating condition classification section is listed separately for normal operating conditions and abnormal extreme operating conditions. The normal operating condition row record: operating condition description "straight track, no braking failure, normal adhesion (dry), AW3 full load, design operating speed 80 km / h", recommended GEBR value 1.165 m / s², applicable scenario "daily operation, tracking interval 90 seconds, ATP normal protection", value based on "efficiency adaptation optimization results, meeting the dual constraints of braking safety and operating interval". Abnormal Extreme Operating Condition Record: The operating condition description is "maximum gradient 30‰, single vehicle 50% brake failure, minimum adhesion 0.18 (wet and slippery), AW3 full load, extreme wind resistance". The recommended GEBR value is 0.945 m / s², applicable scenarios are "severe weather, equipment failure, emergency operation", and the value is based on "safety baseline simulation results to ensure braking capability under extreme operating conditions". The two lines of data clearly distinguish the GEBR grading values ​​under different operating conditions, providing parameter benchmarks for subsequent dynamic adjustments in operation.

[0147] The associated technical parameters section details the complete technical indicator chain corresponding to each GEBR value. For the efficiency adaptation emergency braking rate of 1.165 m / s², the associated parameters include: safe braking distance 283.5 meters, minimum safe protection distance 320.8 meters, tracking interval 89.9 seconds, maximum safe permissible speed 96.8 km / h, brake establishment time 0.9 seconds, and constant braking phase duration 21.3 seconds. For the safety baseline emergency braking rate of 0.945 m / s², the associated parameters include: extreme condition braking distance 312.7 meters, protection distance including safety margin 358.4 meters, usable braking force ratio after fault degradation 91.7%, adhesion utilization rate 89.2%, and gradient resistance compensation 0.294 m / s². The linkage switching threshold section defines the triggering conditions for the ZC-VOBC system to dynamically switch GEBR values ​​between normal and abnormal operating conditions. The switching rules are based on real-time monitoring of environmental and equipment conditions: When the onboard sensors detect a rail adhesion coefficient <0.20 (wet or lightly contaminated), the ZC controller automatically switches from the efficiency-adaptive GEBR 1.165m / s² to the safety baseline GEBR 0.945m / s², and the corresponding ATP overspeed protection curve is recalculated in real time, with the moving authorization MA tightened; when the vehicle braking system diagnostic module reports a failure rate >30% for any bogie braking unit, a switch to the safety baseline GEBR is also triggered; when the line section is identified as a high-gradient section (gradient >25‰) and the train passenger load factor >90%, a preventative switch to the safety baseline GEBR is initiated. The reverse switching conditions are: after 10 consecutive minutes of monitoring, if the adhesion coefficient is >0.22, there are no braking fault alarms, and the track section is straight, the ZC controller reverts to the efficiency-adaptive GEBR, optimizing the operating interval.

[0148] like Figure 2 As shown, Figure 2This demonstration showcases the complete workflow of a simulation method for determining the emergency braking rate (EBR) value in subway systems. First, a standardized input module inputs basic vehicle parameters, line and operational efficiency parameters, and signal safety parameters. Then, an AI big data analysis module intelligently verifies and matches these input parameters to different operating conditions. After categorizing and calibrating the operating conditions, the system automatically identifies normal and abnormal operating conditions. The simulation calculation module loads differentiated calculation models based on the operating condition type. Under normal operating conditions, a preset braking safety model is loaded and coupled with operational efficiency parameters to simultaneously calculate GEBR and tracking interval. Under abnormal operating conditions, only a preset braking safety model is loaded, and operational efficiency parameters are masked to focus on extreme safety conditions. Both types of operating conditions are simulated in parallel. After calculation, a judgment and verification phase is initiated, retrieving similar projects from the historical project database. The dataset utilizes clustering analysis algorithms to determine reasonable ranges for historical safety baseline braking rates and historical efficiency adaptation braking rates. It then performs dual boundary checks on the current simulation-calculated safety baseline emergency protection braking rates (safety baseline braking rate) and efficiency adaptation emergency protection braking rates (adaptation braking rate). If the checks pass, a corresponding list of emergency protection braking rate values ​​is generated, and a final report is output. If any parameter is found to exceed the historical reasonable range, the checks are deemed abnormal, and an intelligent correction process is immediately initiated. Based on historical datasets of similar projects, the basic parameters are automatically corrected, and the simulation calculation steps are re-executed, forming a closed-loop iteration until the checks pass. The data archiving module categorizes and stores input parameters, simulation processes, and value results in the project database, enabling the accumulation and standardized reuse of design experience.

[0149] This application embodiment also provides a device for determining the emergency braking rate value of a subway system. The device includes an acquisition unit, a working condition classification unit, a simulation calculation unit, and an output unit. The acquisition unit acquires basic parameters of the subway project, including vehicle basic parameters, environmental parameters, track parameters, signal safety parameters, and operational efficiency parameters. The working condition classification unit calculates the vehicle basic parameters, track parameters, environmental parameters, and signal safety parameters to obtain the maximum safe permissible speed and minimum safe following interval under the current physical conditions, and compares the maximum safe permissible speed and minimum safe following interval with the operational efficiency parameters to obtain speed margin and interval margin. The device determines the working condition operation type based on the speed margin, interval margin, vehicle basic parameters, track parameters, and environmental parameters. The working condition operation type includes normal operating conditions and abnormal operating conditions. The simulation calculation unit performs the following steps: The unit, if the operating condition is an abnormal operating condition, inputs the vehicle's basic parameters, route parameters, environmental parameters, and signal safety parameters into a preset braking safety model to obtain the safety baseline emergency braking rate; if the operating condition is a normal operating condition, it inputs the vehicle's basic parameters, route parameters, environmental parameters, and signal safety parameters into the preset braking safety model to obtain the initial emergency braking rate; using the operating efficiency parameter as a constraint target, iteratively optimizes the initial emergency braking rate to obtain the efficiency-adapted emergency braking rate; the output unit generates a corresponding emergency braking rate value list based on the safety baseline emergency braking rate and the efficiency-adapted emergency braking rate. The emergency braking rate value list includes the safety baseline emergency braking rate corresponding to the abnormal operating condition and the efficiency-adapted emergency braking rate corresponding to the normal operating condition.

[0150] In one possible implementation, the acquisition unit is used to acquire the braking phase of the train, and calculates the travel distance of the train through each braking phase at the initial speed based on the time delay in the vehicle's basic parameters and signal safety parameters, as well as the gradient resistance and basic resistance in the track parameters and environmental parameters; the operating condition classification unit is used to sum the travel distances of all braking phases to obtain the total safe braking distance at the initial speed; the acquisition unit is used to obtain the maximum permissible braking distance from the track parameters, and when the total safe braking distance equals the maximum permissible braking distance, the initial speed is taken as the maximum safe permissible speed; the operating condition classification unit is used to determine the safety redundancy distance based on the speed and distance measurement error compensation amount and the preset safety margin coefficient in the signal safety parameters; and the total... The minimum safe protection distance is obtained by adding the safe braking distance and the safe redundancy distance. The acquisition unit is used to acquire the stopping time of the preceding vehicle at the platform and the protection travel time of the following vehicle at the target operating speed in the operation efficiency parameters to pass through the minimum safe protection distance. The working condition classification unit is used to determine the difference between the protection braking establishment time of the following vehicle and the acceleration time of the preceding vehicle based on the vehicle's basic parameters and signal safety parameters, and obtain the design time-space difference value. The protection travel time, stopping time and design time-space difference value are added to obtain the minimum safe tracking interval. The speed margin is obtained by subtracting the target operating speed in the operation efficiency parameters from the maximum safe allowable speed. The interval margin is obtained by subtracting the minimum safe tracking interval from the target tracking interval in the operation efficiency parameters.

[0151] In one possible implementation, the acquisition unit is used to acquire the braking system fault modes configured in the vehicle's basic parameters and the design wheel-rail adhesion coefficient set in the environmental parameters; the operating condition classification unit is used to determine that the current simulation operating condition is a failure operating condition in abnormal operating conditions and trigger design risk warning information if the braking system fault mode is a preset critical fault operating condition or the design wheel-rail adhesion coefficient is less than a preset adhesion threshold; if the braking system fault mode is a fault-free normal mode and the design wheel-rail adhesion coefficient is greater than or equal to the preset adhesion threshold, it is determined whether the speed margin and the interval margin are both greater than the preset minimum physical feasible margin value; if the speed margin and the interval margin are both greater than the preset minimum physical feasible margin value, the speed margin, interval margin, vehicle basic parameters, track parameters, and environmental parameters are calculated to obtain a comprehensive risk tension index; when the comprehensive risk tension index is greater than the preset risk threshold, the current simulation operating condition is determined to be an abnormal operating condition; when the comprehensive risk tension index is less than or equal to the preset risk threshold, the current simulation operating condition is determined to be a normal operating condition.

[0152] In one possible implementation, the acquisition unit is used to acquire the braking fault combination mode from the vehicle's basic parameters, and input the braking fault combination mode into the fault degradation correction sub-model to calculate the available braking force output ratio correction value; the simulation calculation unit is used to multiply the available braking force output ratio correction value by the train's rated emergency braking deceleration to obtain the degradation braking deceleration; the wheel-rail adhesion coefficient from the environmental parameters is input into the minimum adhesion constraint sub-model to calculate the maximum available adhesion limit, and the maximum available adhesion limit is used to constrain the amplitude of the degradation braking deceleration to output the adhesion constraint emergency braking deceleration; the vehicle's basic parameters, track parameters, environmental parameters, signal safety parameters, and adhesion constraint emergency braking deceleration are input into the train braking dynamics sub-model to obtain the minimum target deceleration required by the train in the constant braking phase; the minimum target deceleration is input into the safety margin superposition sub-model to output the safety bottom line emergency guarantee braking rate.

[0153] In one possible implementation, the simulation calculation unit is used to input the normal fault-free mode from the vehicle's basic parameters into the fault degradation correction sub-model, determine the available braking force output ratio correction value as a standard lossless coefficient, and multiply the standard lossless coefficient by the train's rated emergency braking deceleration to output the standard emergency braking deceleration; input the wheel-rail adhesion coefficient under normal conditions from the environmental parameters into the minimum adhesion constraint sub-model, calculate the normal adhesion limit, and use the normal adhesion limit to constrain the amplitude of the standard emergency braking deceleration to output the standard adhesion constraint emergency braking deceleration; input the vehicle's basic parameters, track parameters, environmental parameters, signal safety parameters, and the standard adhesion constraint emergency braking deceleration into the train braking dynamics sub-model to obtain the standard target deceleration required by the train under normal operating conditions; input the standard target deceleration into the safety margin superposition sub-model to output the initial emergency protection braking rate.

[0154] In one possible implementation, the simulation calculation unit is used to take the initial emergency braking rate as the current iterative braking rate and set the braking rate adjustment step size; based on the current iterative braking rate, vehicle basic parameters, track parameters, and environmental parameters, it recalculates and sums the current travel distance of the train in each braking stage to obtain the current iterative safe braking distance; it determines the current iterative safe redundancy distance according to the speed and distance measurement error compensation amount and the preset safety margin coefficient in the signal safety parameters, and adds the current iterative safe braking distance to the current iterative safe redundancy distance to obtain the current iterative minimum safe protection distance; it divides the current iterative minimum safe protection distance by the target operating speed to obtain the current iterative protection travel time; and it combines the current iterative protection travel time, the stop time of the preceding train at the platform, and the design time and space... The differences are summed to obtain the current iteration tracking interval. If the current iteration tracking interval is less than or equal to the target tracking interval in the operational efficiency parameters, and the current maximum safe allowable speed is greater than or equal to the target operating speed, then the current iteration braking rate is determined as the efficiency-adaptive emergency braking rate. Here, the current maximum safe allowable speed is derived from the current iteration braking rate. If the current iteration tracking interval is greater than the target tracking interval in the operational efficiency parameters, or the current maximum safe allowable speed is less than the target operating speed, then the current iteration braking rate is cumulatively adjusted using the braking rate adjustment step size, and the adjusted braking rate is used as the new current iteration braking rate. The process returns to recalculate the current iteration safe braking distance until the constraint conditions are met, and the corresponding efficiency-adaptive emergency braking rate is output.

[0155] In one possible implementation, the acquisition unit is used to obtain a historical dataset of similar projects identical to the subway project from a historical project database. This historical dataset includes historical input parameters, historical safety baseline emergency braking rates, and historical efficiency adaptation emergency braking rates. The output unit is used to cluster the historical dataset using a clustering analysis algorithm to determine reasonable ranges for historical safety baseline braking rates and historical efficiency adaptation braking rates. If both the safety baseline emergency braking rate and the efficiency adaptation emergency braking rate are within their respective reasonable ranges, the verification is considered successful, and a corresponding list of emergency braking rate values ​​is generated based on these values. If either the safety baseline emergency braking rate or the efficiency adaptation emergency braking rate is not within its reasonable range, the verification is considered abnormal, and the basic parameters are corrected based on the historical dataset until the verification is successful and the corresponding list of emergency braking rate values ​​is output.

[0156] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0157] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This application provides a schematic diagram of the structure of an electronic device. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 302, and at least one communication bus 305.

[0158] The communication bus 305 is used to enable communication between these components.

[0159] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0160] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0161] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 302, and by calling data stored in memory 302. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and application requests; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0162] The memory 302 may include random access memory (RAM) or read-only memory. Optionally, the memory 302 may include a non-transitory computer-readable storage medium. The memory 302 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 302 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc. The data storage area may store data involved in the various method embodiments described above. Optionally, the memory 302 may also be at least one storage device located remotely from the aforementioned processor 301.

[0163] like Figure 3 As shown, the memory 302, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for determining the value of the subway emergency braking rate.

[0164] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 301 can be used to call the application program stored in the memory 302 to determine the value of the subway emergency braking rate. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0165] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0166] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and practical application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure.

Claims

1. A method for determining the value of emergency braking rate in a subway system, characterized in that, The method includes: Obtain the basic parameters of the subway project, including vehicle basic parameters, environmental parameters, line parameters, signal safety parameters, and operational efficiency parameters; The vehicle's basic parameters, the route parameters, the environmental parameters, and the signal safety parameters are calculated to obtain the maximum safe permissible speed and the minimum safe tracking interval under the current physical conditions. The maximum safe permissible speed and the minimum safe tracking interval are then compared with the operational efficiency parameters to obtain the speed margin and the interval margin. The operating condition type is determined based on the speed margin, the interval margin, the vehicle basic parameters, the route parameters, and the environmental parameters. The operating condition type includes normal operating conditions and abnormal operating conditions. If the operating condition is the abnormal operating condition, the vehicle basic parameters, the route parameters, the environmental parameters, and the signal safety parameters are input into the preset braking safety model to obtain the safety baseline emergency braking rate. If the operating condition is the normal operating condition, then the vehicle basic parameters, the route parameters, the environmental parameters, and the signal safety parameters are input into the preset braking safety model to obtain the initial emergency braking rate. Using the operational efficiency parameter as a constraint target, the initial emergency braking rate is iteratively optimized to obtain an efficiency-adapted emergency braking rate. A corresponding list of emergency braking rate values ​​is generated based on the safety baseline emergency braking rate and the efficiency adaptation emergency braking rate. The list of emergency braking rate values ​​includes the safety baseline emergency braking rate corresponding to the abnormal operating conditions and the efficiency adaptation emergency braking rate corresponding to the normal operating conditions.

2. The method according to claim 1, characterized in that, The calculation of the vehicle's basic parameters, the route parameters, the environmental parameters, and the signal safety parameters yields the maximum safe permissible speed and the minimum safe following interval under the current physical conditions. The maximum safe permissible speed and the minimum safe following interval are then compared with the operational efficiency parameters to obtain speed margin and interval margin. Specifically, this includes: The braking phase of the train is obtained, and based on the time delay in the vehicle basic parameters and the signal safety parameters, as well as the gradient resistance and basic resistance in the track parameters and the environmental parameters, the travel distance of the train through each braking phase at the initial speed is calculated. The total safe braking distance at the initial speed is obtained by summing the travel distances of all braking phases. The maximum permissible braking distance is obtained from the line parameters. When the total safe braking distance is equal to the maximum permissible braking distance, the initial speed is taken as the maximum safe permissible speed. The safety redundancy distance is determined based on the speed and distance measurement error compensation amount and the preset safety margin coefficient in the signal safety parameters; the total safe braking distance is added to the safety redundancy distance to obtain the minimum safe protection distance; Obtain the stopping time of the preceding train at the platform, and obtain the protected travel time of the following train at the target operating speed in the operating efficiency parameters through the minimum safe protection distance; Based on the vehicle's basic parameters and the signal safety parameters, the difference between the rear vehicle's protective braking establishment time and the front vehicle's starting acceleration time is determined to obtain the design spatiotemporal difference. The minimum safe tracking interval is obtained by adding the protective travel time, the stop time, and the designed time-space difference value. The speed margin is obtained by subtracting the target operating speed in the operating efficiency parameter from the maximum safe allowable speed; The minimum safe tracking interval is subtracted from the target tracking interval in the operational efficiency parameter to obtain the interval margin.

3. The method according to claim 2, characterized in that, The determination of the operating condition type based on the speed margin, the interval margin, the vehicle's basic parameters, the route parameters, and the environmental parameters specifically includes: Obtain the braking system fault modes configured in the vehicle's basic parameters, and obtain the design wheel-rail adhesion coefficient set in the environmental parameters; If the braking system failure mode is a preset critical failure condition, or the designed wheel-rail adhesion coefficient is less than a preset adhesion threshold, then the current simulation condition is determined to be a failure operation condition in the abnormal operation condition, and a design risk warning information is triggered. If the braking system failure mode is a fault-free normal mode, and the designed wheel-rail adhesion coefficient is greater than or equal to the preset adhesion threshold, then determine whether the speed margin and the interval margin are both greater than the preset minimum physical feasible margin value. If both the speed margin and the interval margin are greater than the preset minimum physical feasible margin value, then the speed margin, the interval margin, the vehicle basic parameters, the route parameters, and the environmental parameters are calculated to obtain the comprehensive risk tension index. When the comprehensive risk tension index is greater than the preset risk threshold, the current simulation condition is determined to be the abnormal operating condition. When the comprehensive risk tension index is less than or equal to the preset risk threshold, the current simulation condition is determined to be the normal operating condition.

4. The method according to claim 1, characterized in that, The preset braking safety model includes a fault degradation correction sub-model, a minimum adhesion constraint sub-model, a train braking dynamics sub-model, and a safety margin superposition sub-model. If the operating condition is an abnormal operating condition, the vehicle basic parameters, the track parameters, the environmental parameters, and the signal safety parameters are input into the preset braking safety model to obtain the safety baseline emergency braking rate, specifically including: Obtain the braking fault combination mode from the vehicle's basic parameters, and input the braking fault combination mode into the fault degradation correction sub-model to calculate the available braking force output ratio correction value. Multiply the available braking force output ratio correction value by the rated emergency braking deceleration of the train to obtain the degraded braking deceleration; The wheel-rail adhesion coefficient in the environmental parameters is input into the minimum adhesion constraint sub-model to calculate the maximum available adhesion limit. The maximum available adhesion limit is then used to constrain the amplitude of the degraded braking deceleration, and the adhesion constraint emergency braking deceleration is output. The vehicle basic parameters, track parameters, environmental parameters, signal safety parameters, and adhesion-constrained emergency braking deceleration are input into the train braking dynamics sub-model to obtain the minimum target deceleration required by the train during the constant braking phase. The minimum target deceleration is input into the safety margin superposition sub-model, and the safety bottom line emergency protection braking rate is output.

5. The method according to claim 4, characterized in that, If the operating condition is the normal operating condition, then the vehicle basic parameters, the route parameters, the environmental parameters, and the signal safety parameters are input into the preset braking safety model to obtain the initial emergency braking rate, specifically including: The normal fault-free mode in the vehicle's basic parameters is input into the fault degradation correction sub-model. The correction value of the available braking force output ratio is determined to be the standard lossless coefficient. The standard lossless coefficient is then multiplied by the rated emergency braking deceleration of the train to output the standard emergency braking deceleration. The wheel-rail adhesion coefficient under normal conditions in the environmental parameters is input into the minimum adhesion constraint sub-model to calculate the normal adhesion limit. The normal adhesion limit is then used to constrain the amplitude of the standard emergency braking deceleration, and the standard adhesion constraint emergency braking deceleration is output. The vehicle basic parameters, the track parameters, the environmental parameters, the signal safety parameters, and the standard adhesion constraint emergency braking deceleration are input into the train braking dynamics sub-model to obtain the standard target deceleration required by the train under normal operating conditions. The standard target deceleration is input into the safety margin superposition sub-model, and the initial emergency braking rate is output.

6. The method according to claim 5, characterized in that, The step of iteratively optimizing the initial emergency response rate using the operational efficiency parameter as a constraint objective to obtain an efficiency-adaptive emergency response rate specifically includes: The initial emergency braking rate is used as the current iterative braking rate, and the braking rate adjustment step size is set. Based on the current iterative braking rate, the vehicle's basic parameters, the track parameters, and the environmental parameters, the current travel distance of the train in each braking stage is recalculated and summed to obtain the current iterative safe braking distance. The current iteration safety redundancy distance is determined based on the speed and distance measurement error compensation amount and the preset safety margin coefficient in the signal safety parameters. The current iteration safety braking distance is added to the current iteration safety redundancy distance to obtain the current iteration minimum safety protection distance. The current iteration's minimum safe protection distance is divided by the target operating speed to obtain the current iteration's protection travel time; The current iteration protection travel time, the stop time of the preceding vehicle at the platform, and the designed time-space difference are added together to obtain the current iteration tracking interval; If the current iteration tracking interval is less than or equal to the target tracking interval in the operational efficiency parameters, and the current maximum safe allowable speed is greater than or equal to the target operational speed, then the current iteration braking rate is determined as the efficiency adaptation emergency protection braking rate, wherein the current maximum safe allowable speed is derived from the current iteration braking rate; If the current iteration tracking interval is greater than the target tracking interval in the operational efficiency parameter, or the current maximum safe allowable speed is less than the target operating speed, then the braking rate is cumulatively adjusted using the braking rate adjustment step size, and the adjusted braking rate is used as the new current iteration braking rate. The process then returns to recalculating the current iteration safe braking distance until the constraint conditions are met, and the corresponding efficiency-adaptive emergency braking rate is output.

7. The method according to claim 1, characterized in that, The step of generating a corresponding list of emergency braking rate values ​​based on the safety baseline emergency braking rate and the efficiency-adapted emergency braking rate specifically includes: Obtain a dataset of historical similar projects identical to the subway project from the historical project database. The dataset of historical similar projects includes historical input parameters, historical safety baseline emergency braking rate, and historical efficiency adaptation emergency braking rate. Cluster analysis algorithms are used to cluster the historical dataset of similar projects to determine the reasonable range of historical safety baseline braking rate and the reasonable range of historical efficiency matching braking rate. If the safety baseline emergency braking rate is within the reasonable range of the historical safety baseline braking rate, and the efficiency adaptation emergency braking rate is within the reasonable range of the historical efficiency adaptation braking rate, then the verification is deemed successful, and a corresponding list of emergency braking rate values ​​is generated based on the safety baseline emergency braking rate and the efficiency adaptation emergency braking rate. If the safety baseline emergency braking rate is not within the reasonable range of the historical safety baseline braking rate, or the efficiency adaptation emergency braking rate is not within the reasonable range of the historical efficiency adaptation braking rate, then the verification is determined to be abnormal, and the basic parameters are corrected and calculated based on the historical similar project dataset until the verification passes and the corresponding emergency braking rate value list is output.

8. A device for determining the value of emergency braking rate in a subway system, characterized in that, The device includes an acquisition unit, a working condition classification unit, a simulation calculation unit, and an output unit. The acquisition unit acquires basic parameters of the subway project, including vehicle basic parameters, environmental parameters, line parameters, signal safety parameters, and operational efficiency parameters. The operating condition classification unit calculates the maximum safe permissible speed and minimum safe tracking interval under the current physical conditions based on the vehicle's basic parameters, the route parameters, the environmental parameters, and the signal safety parameters. It then compares the maximum safe permissible speed and the minimum safe tracking interval with the operating efficiency parameters to obtain speed margin and interval margin. Based on the speed margin, the interval margin, the vehicle's basic parameters, the route parameters, and the environmental parameters, it determines the operating condition type, which includes normal operating conditions and abnormal operating conditions. If the operating condition is an abnormal operating condition, the simulation calculation unit inputs the vehicle basic parameters, the route parameters, the environmental parameters, and the signal safety parameters into a preset braking safety model to obtain the safety baseline emergency braking rate. If the operating condition is a normal operating condition, the simulation calculation unit inputs the vehicle basic parameters, the route parameters, the environmental parameters, and the signal safety parameters into the preset braking safety model to obtain the initial emergency braking rate. Using the operating efficiency parameter as a constraint target, the initial emergency braking rate is iteratively optimized to obtain an efficiency-adaptive emergency braking rate. The output unit generates a corresponding list of emergency protection braking rate values ​​based on the safety baseline emergency protection braking rate and the efficiency adaptation emergency protection braking rate. The list of emergency protection braking rate values ​​includes the safety baseline emergency protection braking rate corresponding to the abnormal operating conditions and the efficiency adaptation emergency protection braking rate corresponding to the normal operating conditions.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-7.

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