Parameter processing method and device of virtual marshalling train protection model, and storage medium

Abstract

The application provides a parameter processing method and device of a virtual marshalling train protection model, and a storage medium. The method comprises the following steps: acquiring at least one parameter of the virtual marshalling train protection model; for each parameter, if the parameter is a device measurement error related parameter located on a train, the type of the parameter is a measurement error type; if the parameter is a delay related parameter or a direct reading related parameter, the type of the parameter is a direct monitoring type; if the parameter is a braking related parameter, the type of the parameter is an analysis and evaluation type; and monitoring each parameter based on the type of each parameter. The method provided by the application classifies parameters, monitors each parameter according to the type of each parameter, and can effectively avoid potential risks caused by deviations of model parameters in a virtual marshalling operation process.

Description

Technical Field

[0001] This application relates to the field of rail transit technology, and in particular to a parameter processing method, device, and storage medium for a virtual train formation protection model. Background Technology

[0002] Traditional train control systems primarily consider safety performance during the product design phase, taking into account various risks to system operation in advance through methods such as hazard analysis and reliability analysis, and providing preventative measures.

[0003] Product development often fails to consider operational safety, which refers to the identification, prevention, and control of potential safety issues that may arise during the operation of a product or system.

[0004] Therefore, the virtual grouping operation process in the existing scheme may bring potential deviation risks to the model parameters. Summary of the Invention

[0005] To address one of the aforementioned technical deficiencies, this application provides a parameter processing method, device, and storage medium for a virtual train formation protection model.

[0006] The first aspect of this application provides a parameter processing method for a virtual train formation protection model, the method comprising:

[0007] Obtain at least one parameter of the virtual train formation protection model;

[0008] For each parameter, if it is a measurement error related parameter of a device located on the train, then its type is measurement error; if it is a delay related parameter or a directly read related parameter, then its type is direct monitoring; if it is a braking related parameter, then its type is analysis and evaluation.

[0009] Each parameter is monitored based on its type.

[0010] Optionally, each parameter is monitored based on its type, including:

[0011] For measurement error parameters, obtain their current error value and compare it with the reference value; the reference value is provided by the manufacturer of the device to which it belongs.

[0012] For directly monitored parameters, the parameter values ​​at each monitoring time within the monitoring period are obtained based on the corresponding flag bits; the parameters are then monitored based on these values; the flag bits are predefined in the interface corresponding to the parameter.

[0013] For analysis and evaluation parameters, they are monitored according to the train braking phase.

[0014] Optionally, the train is in the braking phase;

[0015] Monitoring is performed on the train during the braking phase, including:

[0016] Obtain the maximum braking acceleration of the vehicle in front during braking;

[0017] Obtain the average braking rate of the following vehicle during the braking process;

[0018] Monitor the maximum and average braking rates.

[0019] Optionally, the train is in the emergency braking establishment phase;

[0020] Monitoring is performed on the train during the braking phase, including:

[0021] Obtain the fitting curve parameters and deviation range corresponding to the current gear of the vehicle; where the fitting curve parameters are obtained based on the deviation between the curve fitting result of the train's historical operation data and a given standard curve;

[0022] The vehicle's safety envelope is determined based on the fitted curve parameters and the deviation range.

[0023] Monitor whether the parameters deviate from the safety envelope.

[0024] Optionally, the train is in the process of entering and stopping at the station;

[0025] Monitoring is performed on the train during the braking phase, including:

[0026] Acquire train speed and acceleration data during normal operating hours;

[0027] Plot the braking process curve based on velocity and acceleration data;

[0028] After dividing the braking process curve into multiple line segments, multi-segment fitting is performed.

[0029] Identify the fitted line segment with the largest negative slope;

[0030] The right endpoint of the fitted line segment is identified as the inflection point where the tail rises.

[0031] Optionally, after identifying the right endpoint of the determined fitted line segment as the inflection point where the tail-up occurs, the method further includes:

[0032] Correct the braking rate corresponding to the inflection point.

[0033] Optionally, after monitoring each parameter based on its type, the process also includes:

[0034] For parameters that are used for analysis and evaluation, if they are parameters that are used when the train is braking, then parameter optimization is performed.

[0035] Optionally, parameter optimization may be performed, including:

[0036] Maintain a higher braking rate for the vehicle in front than for the vehicle behind.

[0037] When the train speed is not higher than the fixed value given in the low-speed stage, the lower limit of the high gear of the rear car is the fixed value of the rear car, and the upper limit of the low gear of the front car is the fixed value of the front car; the fixed value of the rear car is the nominal value used for the high gear of the rear car, and satisfies the safety assumptions of the virtual train formation protection model; the fixed value of the front car is the nominal value used for the low gear of the front car, and satisfies the safety assumptions of the virtual train formation protection model.

[0038] When the train speed is higher than a fixed value given in the low-speed phase, the lower limit curve for the high-speed position of the following train is -k. v下 *Train speed-b 下 +Delta 下 The upper limit curve for the low gear of the preceding vehicle is -k. v上 *Train speed-b 上 +Delta 上 ;

[0039] Where, k v下 k v上 b 下 b 上 Delta 下 Delta 上 These are pre-set parameter values.

[0040] A second aspect of this application provides an electronic device, comprising:

[0041] Memory;

[0042] Processor; and

[0043] Computer programs;

[0044] The computer program is stored in the memory and configured to be executed by the processor to implement the method described in the first aspect above.

[0045] A third aspect of this application provides a computer-readable storage medium having a computer program stored thereon; the computer program is executed by a processor to implement the method described in the first aspect above.

[0046] This application provides a parameter processing method, device, and storage medium for a virtual train formation protection model. The method acquires at least one parameter of the virtual train formation protection model. For each parameter, if it is a measurement error related parameter of a device located on the train, its type is classified as measurement error; if it is a delay-related parameter or a directly read parameter, its type is classified as direct monitoring; if it is a braking-related parameter, its type is classified as analysis and evaluation. Based on the type of each parameter, the method monitors each parameter. The method provided in this application classifies parameters and monitors them according to their type, which can effectively avoid potential risks caused by deviations in model parameters during virtual train formation operation. Attached Figure Description

[0047] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0048] Figure 1 A flowchart illustrating the parameter processing method for the virtual train formation protection model provided in this application embodiment;

[0049] Figure 2 A schematic diagram of fitting parameters and safety boundaries for a braking establishment curve provided in this application embodiment;

[0050] Figure 3 This is a schematic diagram of a tail-up analysis result provided in an embodiment of this application;

[0051] Figure 4 A schematic diagram illustrating the statistical results of emergency braking rates in high and low gears, provided for an embodiment of this application;

[0052] Figure 5 A schematic diagram of the linear fitting results of emergency braking rates in high and low gears at a 95% confidence level, provided for an embodiment of this application.

[0053] Figure 6 A schematic diagram of the linear fitting results of the emergency braking rate of high and low gears at a 99% confidence level, provided for an embodiment of this application.

[0054] Figure 7 A schematic diagram of the linear fitting results of the emergency braking rate in high and low gears at a 99.9% confidence level, provided for the embodiments of this application;

[0055] Figure 8 A schematic diagram of the linear fitting result of the emergency braking rate for a first scheme for determining the upper limit curve value of the low gear of the front vehicle provided in an embodiment of this application.

[0056] Figure 9A schematic diagram of the linear fitting result of the emergency braking rate for the second scheme of taking the upper limit curve value of the low gear of the front vehicle provided in the embodiment of this application;

[0057] Figure 10 A schematic diagram of the front and rear vehicle safety distance after curve optimization is provided for an embodiment of this application;

[0058] Figure 11 This is an application scenario diagram of the parameter processing method for the virtual train formation protection model provided in the embodiments of this application. Detailed Implementation

[0059] To make the technical solutions and advantages of the embodiments of this application clearer, the exemplary embodiments of this application will be described in further detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0060] In developing this application, the inventors discovered that traditional train control systems primarily consider safety performance during the product design phase, proactively addressing various operational risks and providing preventative measures through hazard analysis and reliability analysis. However, operational safety is often neglected during product development. Operational safety, in this context, refers to the identification, prevention, and control of potential safety issues that may arise during product or system use. Therefore, the virtual train formation operation process in existing solutions carries the potential risk of deviations in model parameters.

[0061] To address the aforementioned issues, this application provides a parameter processing method, device, and storage medium for a virtual train formation protection model. The method acquires at least one parameter of the virtual train formation protection model. For each parameter, if it is a measurement error related parameter of a device located on the train, its type is classified as measurement error; if it is a delay-related parameter or a directly read parameter, its type is classified as direct monitoring; if it is a braking-related parameter, its type is classified as analysis and evaluation. Based on the type of each parameter, the method monitors each parameter. The method provided in this application classifies parameters and monitors them according to their type, effectively avoiding potential risks caused by deviations in model parameters during virtual train formation operation.

[0062] See Figure 1 The parameter processing method for the virtual train formation protection model provided in this embodiment is implemented as follows:

[0063] 101. Obtain at least one parameter of the virtual train formation protection model.

[0064] Among them, the virtual train formation protection model is the virtual train formation "collision with soft wall" model.

[0065] In the virtual train formation protection model (i.e., the virtual train formation "collision with soft wall" model), assumptions are made about the train's status acquisition (speed, acceleration, impact rate, etc.), braking performance parameters, and various delay parameters. These parameters are positioned as safety parameters. The assumptions of safety parameters may deviate from the actual performance of the train. The deviation must meet the range required by the model; otherwise, it will cause certain risks.

[0066] The parameters in this step are the same as those in the virtual train formation protection model.

[0067] 102. For each parameter, if it is a measurement error related parameter of a device located on the train, then its type is measurement error; if it is a delay related parameter or a directly read related parameter, then its type is direct monitoring; if it is a braking related parameter, then its type is analysis and evaluation.

[0068] This step will categorize each parameter.

[0069] The parameters in the virtual train formation protection model can be divided into three categories according to the different monitoring schemes: measurement error type, direct monitoring type, and analysis and evaluation type.

[0070] 1. Parameters belonging to the category of measurement error are related to the measurement error of devices located on the train, such as the measurement accuracy parameters of speed and distance measuring devices, accelerometers, and positioning sensors.

[0071] 2. Parameters that fall under the category of direct monitoring are either delay-related parameters or directly readable parameters. For example, delay-related parameters include command transmission delay, traction cut-off delay, and brake establishment delay. Directly readable parameters include parameters such as the maximum traction acceleration and maximum impact rate during the brake establishment phase.

[0072] 3. Parameters belonging to the analysis and evaluation category are braking-related parameters, such as braking rate parameters for high and low gears, deviation parameters during the emergency braking establishment phase, and parameters related to low-speed tail-lifting phenomenon.

[0073] 103. Monitor each parameter based on its type.

[0074] Different monitoring schemes are required for different types of parameters.

[0075] Specifically,

[0076] 1. For measurement error parameters

[0077] For example, for parameters such as the measurement accuracy of speed and distance measuring devices, accelerometers, and positioning sensors, there are currently no effective means of measuring such measurement errors. At present, the reference error provided by the manufacturer is used. Therefore, for measurement error parameters, the monitoring scheme is as follows: obtain the current error value of the parameter (i.e., the currently monitored measurement error parameter) and compare the error value with the reference value.

[0078] The reference values ​​are provided by the manufacturer of the device to which it belongs.

[0079] 2. For directly monitored parameters

[0080] For example, parameters such as command transmission delay, traction cut-off delay, and brake establishment delay can be obtained by defining flag bits in the system interface and then counting the number of timestamps are sent and received. Parameters such as maximum traction acceleration and maximum impact rate during the brake establishment phase can be statistically analyzed in real time using accelerometers and differential processing.

[0081] Therefore, for directly monitored parameters, the monitoring scheme is as follows: For directly monitored parameters, based on the corresponding flag bits, obtain the parameter values ​​of the parameter (i.e., the currently monitored directly monitored parameter) at each monitoring time within the monitoring period. Monitor the parameter (i.e., the currently monitored directly monitored parameter) based on these parameter values.

[0082] The flag is predefined in the interface corresponding to the parameter.

[0083] The specific monitoring plan is shown in Table 1:

[0084] Table 1. Monitoring Scheme for Directly Monitored Parameters (Example)

[0085]

[0086] 3. For analysis and evaluation parameters

[0087] For example, the monitoring scheme for parameters such as braking rate at high and low gears, deviation parameters during the emergency braking establishment phase, and parameters related to low-speed tail-end phenomenon is as follows: monitor them according to the train braking phase (i.e., the currently monitored analysis and evaluation parameters).

[0088] The train braking phase includes: the train in the braking phase, the train in the emergency braking establishment phase, and the train in the station entry and stopping phase.

[0089] Specifically,

[0090] 1) If the train is in the braking phase

[0091] When a train is braking, the monitored parameter can be the deviation of the emergency braking rate under different gears. During train tracking or formation, for safety reasons, different gears are set for the braking capabilities of the preceding and following trains, typically high / low / default gears. In formation operation, the preceding train uses a low gear and the following train uses a high gear. In the virtual formation train protection model, the braking rate values ​​for the high and low gears of the preceding and following trains are taken from the nominal values ​​and deviation ranges provided by the vehicles. Therefore, it is necessary to monitor the braking rate of the train in operation. The monitoring methods for the preceding and following trains are different: the preceding train monitors the maximum braking acceleration during the braking process, while the following train monitors the average braking rate.

[0092] Therefore, if the train is in the braking phase, the monitoring process is as follows:

[0093] (1) Obtain the maximum value of the braking acceleration of the vehicle in front during the braking process.

[0094] (2) Obtain the average braking rate of the following vehicle during the braking process.

[0095] (3) Monitor the maximum value and average braking rate.

[0096] 2) If the train is in the emergency braking establishment phase

[0097] During the emergency braking initiation phase, the train provides the fitting curve parameters and deviation range for the corresponding gear. Figure 2 (As shown by the solid line in the middle), a safety envelope (shaded area) is drawn based on the parameters provided by the vehicle. During the establishment phase, it should be ensured that the train's acceleration value always remains within the safety envelope. The fitted parameters need to be evaluated offline after operation. The evaluation method is to perform curve fitting based on the operating data and analyze the deviation from the given standard curve; if the curve deviates from the safety envelope, an alarm will be issued.

[0098] Therefore, if the train is in the emergency braking initiation phase, the monitoring process is as follows:

[0099] (1) Obtain the fitting curve parameters and deviation range corresponding to the current gear of the vehicle.

[0100] The fitting curve parameters are obtained by comparing the curve fitting results based on historical train operation data with the deviation of a given standard curve.

[0101] (2) Determine the safety envelope of the vehicle based on the parameters of the fitted curve and the deviation range.

[0102] (3) Monitor whether the parameters deviate from the safety envelope.

[0103] 3) If the train is in the process of entering and stopping at the station,

[0104] Trains are prone to exhibiting a low-speed tail-end lift during the station-entry and stopping phase. Therefore, if a train is in the station-entry and stopping phase, the monitoring process is as follows:

[0105] (1) Obtain the speed and acceleration data of the train during normal operation.

[0106] For example, select speed and acceleration data during normal train operation within a certain operating period (all day).

[0107] (2) Draw the braking process (va) curve based on velocity and acceleration data.

[0108] (3) After dividing the braking process curve into multiple line segments, perform multi-line segment fitting.

[0109] (4) Determine the fitted line segment with the largest negative slope.

[0110] (5) Identify the right endpoint of the fitted line segment as the inflection point where the tail rises.

[0111] Since the characteristics of the train braking process curve are highly similar under normal circumstances, the braking process (va) curve is divided into several segments. Then, multi-segment fitting is performed based on actual data, and the inflection point of the tail-up is identified based on the slope change of the segment.

[0112] like Figure 3 As shown, the inflection point judgment condition is: k = arg max{-R1, -R2, -R3, ..., -R n}, where R i The slope of the i-th fitted line segment in the direction from low speed to high speed is represented by , n is the number of all fitted line segments, arg max represents the index of the line segment that makes the negative value of the slope of all line segments the largest, and the tail occurs at the right endpoint of the k-th line segment.

[0113] In addition, in order to reduce the tail-end phenomenon, after executing (5), the braking rate corresponding to the inflection point can be corrected based on long-term statistical results.

[0114] In addition, after monitoring the analysis and evaluation parameters in step 103, parameter optimization will be performed on the analysis and evaluation parameters that belong to the braking phase of the train.

[0115] During optimization, a simplified relative braking model is considered. At time t0, the leading vehicle has an initial velocity v. l With average emergency braking rate a l To stop, follow the vehicle with an initial velocity v f With average emergency braking rate a f The two vehicles are brought to a stop with a distance of s0 between them. The fact that the two vehicles do not collide during the entire braking process can be expressed as follows:

[0116] s l (t)-s f (t)+s0>0

[0117] If the braking rate of the preceding vehicle is always greater than that of the following vehicle, i.e., a l >a f If the two vehicles brake simultaneously, no collision will occur. The condition for no collision simplifies to the two vehicles not overlapping after they stop, i.e.:

[0118] s l -s f +s0>0

[0119] Therefore, the limit of the distance between two vehicles before emergency braking can be simplified as the difference between the emergency braking distance of the following vehicle and the emergency braking distance of the preceding vehicle, that is:

[0120] s0 = s f -s l

[0121] Substituting the emergency braking rate and initial velocity into the above equation:

[0122]

[0123] Considering the stable tracking phase of the two vehicles, v can be considered... l ≈v f =v0, then the above formula can be written as:

[0124]

[0125] In a l >a f Under the given conditions, if we want to make s0 smaller, we need to make a smaller. l -a f The smaller and a l a f The larger the value, the more likely it is to be. This means that the braking performance of the two vehicles should be made more consistent, and the emergency braking rate of the two vehicles should be biased towards a larger value.

[0126] Therefore, the process of parameter optimization is as follows:

[0127] 1. Maintain a higher braking rate for the vehicle in front than for the vehicle behind.

[0128] 2. When the train speed is not higher than a fixed value given in the low-speed phase, the lower limit of the high gear of the rear train is the fixed value of the rear train, and the upper limit of the low gear of the front train is the fixed value of the front train. The fixed value of the rear train is the nominal value used for the high gear of the rear train and satisfies the safety assumptions of the virtual train formation protection model. The fixed value of the front train is the nominal value used for the low gear of the front train and satisfies the safety assumptions of the virtual train formation protection model.

[0129] 3. When the train speed is higher than the fixed value given in the low-speed phase, the lower limit curve of the high-speed position of the following train is -k. v下 *Train speed-b 下 +Delta 下 The upper limit curve for the low gear of the preceding vehicle is -k. v上 *Train speed-b 上 +Delta 上 .

[0130] Where, k v下 k v上 b 下 b 上 Delta 下 Delta 上 These are pre-set parameter values.

[0131] For example,

[0132] If the preceding vehicle uses a nominal value of -137 in low gear with a 12% upper and lower limit, and the following vehicle uses a nominal value of -134.4 in high gear with a 12% upper and lower limit, the deviation of the currently used 12% nominal value (e.g.) Figure 4 The area from -137 to -134.4 meets the safety assumptions of the existing virtual train formation protection model (i.e., the virtual train formation "collision with soft wall" model), but has room for optimization.

[0133] Provided that the braking rate of the vehicle in front is greater than that of the vehicle behind, the smaller the difference in braking rate between high and low gears, the better, and the braking rates of the two vehicles should be chosen towards the larger value.

[0134] Because the sample dispersion is higher at low speeds than at high speeds, the distribution pattern is poorer, and the fitted curve is not significantly different from the given upper and lower limits, a piecewise function is used to define the upper and lower limits of the gear. For the following vehicle (higher gear), the low-speed stage (e.g., 30 km / h) is a given fixed value. Therefore, when the train speed is not higher than the given fixed value for the low-speed stage (e.g., 30 km / h), the lower limit of the higher gear for the following vehicle is the fixed value for the following vehicle, and the upper limit of the lower gear for the preceding vehicle is the fixed value for the preceding vehicle. When the train speed is higher than the given fixed value for the low-speed stage (e.g., 30 km / h), the curve for the lower limit of the higher gear for the following vehicle is -k. v下 *Train speed-b 下 +Delta 下 The upper limit curve for the low gear of the preceding vehicle is -k. v上 *Train speed-b 上 +Delta 上 .

[0135] For example,

[0136] The value of the rear vehicle is fixed at -134.4, k. v下 The value is 0.165877, the train speed is v0, and b 下Delta is 139.772172. 下 If the value is 10.348, then the lower limit curve for the high gear of the following vehicle is...

[0137]

[0138] The fitting results of the above-mentioned rear vehicle high gear lower limit curve for high and low gear emergency braking rate at 95% confidence level are as follows: Figure 5 As shown, the fitting results for the emergency braking rates of high and low gears at a 99% confidence level are as follows: Figure 6 As shown, the fitting results for the emergency braking rates of high and low gears at a 99.9% confidence level are as follows: Figure 7 As shown.

[0139] exist Figure 5 , Figure 6 and Figure 7 In the figure, the confidence level of the slashed part is around 98%.

[0140] The value of the upper limit curve for the low gear of the preceding vehicle is relatively flexible. This embodiment provides two main value schemes.

[0141] In Scheme 1 for determining the upper limit curve of the low gear of the preceding vehicle, the fixed value given in the low-speed stage is 30km / h, and the fixed value of the preceding vehicle is -137, k v上 The value is 0.165877, the train speed is v0, and b 上 Delta is 139.772172. 上 If the value is 7.748, then the curve for the upper limit of the low gear of the preceding vehicle is:

[0142]

[0143] The linear fitting results of the emergency braking rate of the above-mentioned low-gear upper limit curve of the preceding vehicle are as follows: Figure 8 As shown.

[0144] In Scheme Two for determining the upper limit curve value of the low gear of the preceding vehicle, the fixed value given for the low speed stage is 49.3 km / h, and the fixed value for the preceding vehicle is -137 km / h. v上 The value is 0.165877, the train speed is v0, and b 上 Delta is 139.772172. 上 If the value is 7.748, then the curve for the upper limit of the low gear of the preceding vehicle is:

[0145]

[0146] The linear fitting results of the emergency braking rate of the above-mentioned low-gear upper limit curve of the preceding vehicle are as follows: Figure 9 As shown.

[0147] Under the condition that the speeds of the preceding and following trains are the same and both use a 12% braking deviation, the safe distance between the preceding and following trains is calculated based on the optimized train curve and input into the virtual train formation protection model. Figure 10 As shown, the results indicate that the optimized curve parameters theoretically reduce the train safety distance by approximately 46%.

[0148] The practical application scenarios of the parameter processing method for the virtual train formation protection model provided in this embodiment can be as follows: Figure 11 As shown, this can avoid the potential risks caused by deviations in model parameters during virtual train formation operation. In addition, the safety model can be updated in a timely (regular) manner based on the analysis results. At the same time, the braking parameters in the "collision with soft wall" protection of the virtual train formation protection model can be optimized to make the braking performance of the train formation more consistent and further shorten the safe operating interval.

[0149] This embodiment provides a parameter processing method for a virtual train formation protection model, which obtains at least one parameter of the virtual train formation protection model. For each parameter, if it is a parameter related to the measurement error of a device located on the train, its type is measurement error; if it is a time delay related parameter or a directly read related parameter, its type is direct monitoring; if it is a braking related parameter, its type is analysis and evaluation. Based on the type of each parameter, monitoring each parameter can effectively avoid the potential risks caused by the deviation of model parameters during virtual train formation operation.

[0150] Based on the same inventive concept as the parameter processing method of the virtual train formation protection model, this embodiment provides an electronic device, which includes: a memory, a processor, and a computer program.

[0151] The computer program is stored in memory and configured to be executed by a processor to implement the parameter processing method of the aforementioned virtual train formation protection model.

[0152] Specifically,

[0153] Obtain at least one parameter of the protection model of the virtual train formation.

[0154] For each parameter, if it is a measurement error related parameter of a device located on the train, then its type is measurement error. If it is a delay-related parameter or a directly read parameter, then its type is direct monitoring. If it is a braking-related parameter, then its type is analysis and evaluation.

[0155] Each parameter is monitored based on its type.

[0156] Optionally, each parameter is monitored based on its type, including:

[0157] For measurement error parameters, obtain their current error value and compare it with a reference value. The reference value is provided by the manufacturer of the device.

[0158] For directly monitored parameters, the parameter values ​​at each monitoring moment within the monitoring period are obtained based on the corresponding flag bits. Monitoring is then performed based on these parameter values. The flag bits are predefined in the interface corresponding to the parameter.

[0159] For analysis and evaluation parameters, they are monitored according to the train braking phase.

[0160] Optionally, the train is in the braking phase.

[0161] Monitoring is performed on the train during the braking phase, including:

[0162] Obtain the maximum braking acceleration of the vehicle in front during braking.

[0163] Obtain the average braking rate of the following vehicle during the braking process.

[0164] Monitor the maximum and average braking rates.

[0165] Optionally, the train is in the emergency braking initiation phase.

[0166] Monitoring is performed on the train during the braking phase, including:

[0167] Obtain the fitting curve parameters and deviation range corresponding to the vehicle's current gear. The fitting curve parameters are obtained based on the deviation between the curve fitting results from historical train operation data and a given standard curve.

[0168] The safety envelope of the vehicle is determined based on the parameters of the fitted curve and the deviation range.

[0169] Monitor whether the parameters deviate from the safety envelope.

[0170] Optionally, the train is in the process of entering and stopping at the station.

[0171] Monitoring is performed on the train during the braking phase, including:

[0172] Acquire train speed and acceleration data during normal operating hours.

[0173] The braking process curve is plotted based on velocity and acceleration data.

[0174] After dividing the braking process curve into multiple line segments, multi-segment fitting is performed.

[0175] Determine the fitted line segment with the largest negative slope.

[0176] The right endpoint of the fitted line segment is identified as the inflection point where the tail rises.

[0177] Optionally, after identifying the right endpoint of the determined fitted line segment as the inflection point where the tail-up occurs, the method further includes:

[0178] Correct the braking rate corresponding to the inflection point.

[0179] Optionally, after monitoring each parameter based on its type, the process also includes:

[0180] For parameters that are used for analysis and evaluation, if they are parameters that are used when the train is braking, then parameter optimization is performed.

[0181] Optionally, parameter optimization may be performed, including:

[0182] Maintain a higher braking rate for the vehicle in front than for the vehicle behind.

[0183] When the train speed is no higher than a fixed value given in the low-speed phase, the lower limit of the high gear of the rear train is the fixed value of the rear train, and the upper limit of the low gear of the front train is the fixed value of the front train. The fixed value of the rear train is the nominal value used for the high gear of the rear train and satisfies the safety assumptions of the virtual train formation protection model. The fixed value of the front train is the nominal value used for the low gear of the front train and satisfies the safety assumptions of the virtual train formation protection model.

[0184] When the train speed is higher than a fixed value given in the low-speed phase, the lower limit curve for the high-speed position of the following train is -k. v下 *Train speed-b 下 +Delta 下 The upper limit curve for the low gear of the preceding vehicle is -k. v上 *Train speed-b 上 +Delta 上 .

[0185] Where, k v下 k v上 b 下 b 上 Delta 下 Delta 上 These are pre-set parameter values.

[0186] The electronic device provided in this embodiment has a computer program executed by a processor to classify parameters and monitor each parameter according to its type, which can effectively avoid the potential risks caused by deviations in model parameters during virtual grouping operation.

[0187] Based on the same inventive concept as the parameter processing method for the virtual train formation protection model, this embodiment provides a computer-readable storage medium on which a computer program is stored. The computer program is executed by a processor to implement the aforementioned parameter processing method for the virtual train formation protection model.

[0188] Specifically,

[0189] Obtain at least one parameter of the protection model of the virtual train formation.

[0190] For each parameter, if it is a measurement error related parameter of a device located on the train, then its type is measurement error. If it is a delay-related parameter or a directly read parameter, then its type is direct monitoring. If it is a braking-related parameter, then its type is analysis and evaluation.

[0191] Each parameter is monitored based on its type.

[0192] Optionally, each parameter is monitored based on its type, including:

[0193] For measurement error parameters, obtain their current error value and compare it with a reference value. The reference value is provided by the manufacturer of the device.

[0194] For directly monitored parameters, the parameter values ​​at each monitoring moment within the monitoring period are obtained based on the corresponding flag bits. Monitoring is then performed based on these parameter values. The flag bits are predefined in the interface corresponding to the parameter.

[0195] For analysis and evaluation parameters, they are monitored according to the train braking phase.

[0196] Optionally, the train is in the braking phase.

[0197] Monitoring is performed on the train during the braking phase, including:

[0198] Obtain the maximum braking acceleration of the vehicle in front during braking.

[0199] Obtain the average braking rate of the following vehicle during the braking process.

[0200] Monitor the maximum and average braking rates.

[0201] Optionally, the train is in the emergency braking initiation phase.

[0202] Monitoring is performed on the train during the braking phase, including:

[0203] Obtain the fitting curve parameters and deviation range corresponding to the vehicle's current gear. The fitting curve parameters are obtained based on the deviation between the curve fitting results from historical train operation data and a given standard curve.

[0204] The safety envelope of the vehicle is determined based on the parameters of the fitted curve and the deviation range.

[0205] Monitor whether the parameters deviate from the safety envelope.

[0206] Optionally, the train is in the process of entering and stopping at the station.

[0207] Monitoring is performed on the train during the braking phase, including:

[0208] Acquire train speed and acceleration data during normal operating hours.

[0209] The braking process curve is plotted based on velocity and acceleration data.

[0210] After dividing the braking process curve into multiple line segments, multi-segment fitting is performed.

[0211] Determine the fitted line segment with the largest negative slope.

[0212] The right endpoint of the fitted line segment is identified as the inflection point where the tail rises.

[0213] Optionally, after identifying the right endpoint of the determined fitted line segment as the inflection point where the tail-up occurs, the method further includes:

[0214] Correct the braking rate corresponding to the inflection point.

[0215] Optionally, after monitoring each parameter based on its type, the process also includes:

[0216] For parameters that are used for analysis and evaluation, if they are parameters that are used when the train is braking, then parameter optimization is performed.

[0217] Optionally, parameter optimization may be performed, including:

[0218] Maintain a higher braking rate for the vehicle in front than for the vehicle behind.

[0219] When the train speed is no higher than a fixed value given in the low-speed phase, the lower limit of the high gear of the rear train is the fixed value of the rear train, and the upper limit of the low gear of the front train is the fixed value of the front train. The fixed value of the rear train is the nominal value used for the high gear of the rear train and satisfies the safety assumptions of the virtual train formation protection model. The fixed value of the front train is the nominal value used for the low gear of the front train and satisfies the safety assumptions of the virtual train formation protection model.

[0220] When the train speed is higher than a fixed value given in the low-speed phase, the lower limit curve for the high-speed position of the following train is -k. v下 *Train speed-b 下 +Delta 下 The upper limit curve for the low gear of the preceding vehicle is -k. v上 *Train speed-b 上 +Delta 上 .

[0221] Where, k v下 kv上 b 下 b 上 Delta 下 Delta 上 These are pre-set parameter values.

[0222] The computer-readable storage medium provided in this embodiment has a computer program thereon that is executed by a processor to classify parameters and monitor each parameter according to its type, which can effectively avoid the potential risks caused by deviations in model parameters during virtual grouping operations.

[0223] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0224] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0225] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0226] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0227] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0228] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A parameter processing method for a virtual train formation protection model, characterized in that, The method includes: Obtain at least one parameter of the virtual train formation protection model; For each parameter, if it is a measurement error related parameter of a device located on the train, then its type is measurement error; if it is a delay related parameter or a directly read related parameter, then its type is direct monitoring; if it is a braking related parameter, then its type is analysis and evaluation. Based on the type of each parameter, each parameter is monitored, including: For measurement error parameters, obtain their current error value and compare the error value with a reference value; wherein the reference value is provided by the manufacturer of the device to which it belongs; For directly monitored parameters, the parameter values ​​at each monitoring moment within the monitoring period are obtained based on the corresponding flag bits; the parameters are then monitored based on these values; wherein, the flag bits are predefined in the interface corresponding to the parameter. For analysis and evaluation parameters, monitoring is performed according to the train braking phase. Specifically, when the train is in the braking phase, this includes obtaining the maximum braking acceleration of the preceding train and the average braking rate of the following train. When the train is in the station-entry stopping phase, this includes: obtaining the train's speed and acceleration data, and plotting a braking process curve based on the speed and acceleration data; dividing the braking process curve into multiple segments and performing multi-segment fitting; determining the fitted segment with the largest negative slope, and identifying the right endpoint of the determined fitted segment as the inflection point where the tail-end occurs; wherein the train is in the emergency braking establishment phase. The monitoring based on the train braking phase includes: Obtain the fitting curve parameters and deviation range corresponding to the current gear of the vehicle; wherein, the fitting curve parameters are obtained based on the deviation between the curve fitting result of the train's historical operation data and a given standard curve; The vehicle's safety envelope is determined based on the fitted curve parameters and the deviation range. Monitor whether the parameters deviate from the safety envelope; The train is in the process of entering and stopping at the station; The monitoring based on the train braking phase includes: Acquire train speed and acceleration data during normal operating hours; Based on the speed and acceleration data, a braking process curve is plotted. After dividing the braking process curve into multiple line segments, multi-segment fitting is performed. Identify the fitted line segment with the largest negative slope; The right endpoint of the fitted line segment is identified as the inflection point where the tail rises. For analysis and evaluation parameters, if they pertain to parameters during the train's braking phase, parameter optimization is performed. This optimization includes: maintaining a greater braking rate for the preceding train than for the following train; when the train speed is not higher than a fixed value given in the low-speed phase, the lower limit of the following train's high gear is the fixed value for the following train, and the upper limit of the preceding train's low gear is the fixed value for the preceding train; the fixed value for the following train is the nominal value used for the high gear of the following train, and it satisfies the safety assumptions of the virtual train formation protection model; the fixed value for the preceding train is the nominal value used for the low gear of the preceding train, and it satisfies the safety assumptions of the virtual train formation protection model; when the train speed is higher than the fixed value given in the low-speed phase, the lower limit curve of the following train's high gear is -k. v下 Train speed -b 下 +Delta 下 The upper limit curve for the low gear of the preceding vehicle is -k. v上 Train speed -b 上 +Delta 上 ; where k v下 k v上 b 下 b 上 Delta 下 Delta 上 These are pre-set parameter values; The virtual train formation protection model is updated based on the monitoring and analysis results.

2. The method according to claim 1, characterized in that, After identifying the right endpoint of the determined fitted line segment as the inflection point where the tail-up occurs, the method further includes: Correct the braking rate corresponding to the inflection point.

3. An electronic device, characterized in that, include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in any one of claims 1-2.

4. A computer-readable storage medium, characterized in that, It stores a computer program thereon; the computer program is executed by a processor to implement the method as described in any one of claims 1-2.

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