Railway vehicle air supply system detection method and system, train, equipment and medium

Abstract

The invention provides a detection method and system for an air supply system of a railway vehicle, a train, equipment and a medium, and can be applied to the technical field of railway vehicles. The method comprises the following steps: in response to determining that a to-be-detected vehicle is in a static state, determining a vehicle working condition of the to-be-detected vehicle according to a vehicle speed data set and a first main blast pipe pressure data set of the to-be-detected vehicle within a first preset time period; under the condition that the vehicle working condition represents that the to-be-detected vehicle is in the stable working condition, according to a wind using equipment data set of the to-be-detected vehicle in a second preset time period, the wind using equipment state of the to-be-detected vehicle is determined, and the second preset time period is after the first preset time period; and under the condition that the state of the air using equipment represents that the air using equipment of the to-be-detected vehicle does not work, determining an air tightness detection result aiming at the air supply system according to a second main air pipe pressure data set in a second preset time period.

Description

Technical Field

[0001] This disclosure relates to the field of rail vehicle technology, and more specifically, to a detection method, system, train, equipment, and medium for a rail vehicle air supply system. Background Technology

[0002] The air supply system of rail transit vehicles provides air to the equipment using compressed air. When the air supply system malfunctions, insufficient air supply and increased air consumption will occur, resulting in abnormal rates of rise and fall of total air pressure, and the equipment will be unable to operate normally.

[0003] Therefore, to ensure the normal operation of ventilation equipment, it is necessary to regularly test the air tightness of the air supply system to ensure that there are no obvious leaks in the air ducts and ventilation equipment. The traditional method of testing the air supply system is manual testing, which requires manual confirmation of the vehicle's stationary state and the air tightness test of the air supply system. This method is not only inefficient, but also fails to detect abnormalities in the air supply system in a timely manner. Summary of the Invention

[0004] In view of this, the present disclosure provides a detection method, system, train, equipment and medium for a rail vehicle air supply system.

[0005] One aspect of this disclosure provides a method for detecting a rail vehicle air supply system, comprising: in response to determining that the vehicle to be tested is stationary, determining the vehicle operating condition of the vehicle to be tested based on a vehicle speed dataset and a first total air duct pressure dataset within a first preset time period; if the vehicle operating condition indicates that the vehicle to be tested is in a stable operating condition, determining the air-using equipment status of the vehicle to be tested based on an air-using equipment dataset of the vehicle to be tested within a second preset time period, wherein the second preset time period is after the first preset time period; if the air-using equipment status indicates that the air-using equipment of the vehicle to be tested is not working, determining an airtightness test result for the air supply system based on a second total air duct pressure dataset within the second preset time period.

[0006] According to an embodiment of this disclosure, determining the vehicle operating condition of the vehicle to be tested based on the vehicle speed dataset and the first total duct pressure dataset within a first preset time period includes: determining the maximum value and the minimum value of the first total duct pressure from multiple first total duct pressure data in the first total duct pressure dataset; and determining that the vehicle to be tested is in a stable operating condition when the difference between the maximum value and the minimum value of the first total duct pressure is less than a first preset threshold and multiple vehicle speed data in the vehicle speed dataset are all zero.

[0007] According to embodiments of this disclosure, the aforementioned air-using equipment dataset includes an equipment status dataset and an equipment pressure dataset. Determining the air-using equipment status of the vehicle under test based on the air-using equipment dataset of the vehicle under test within a second preset time period includes: determining a first equipment sub-state based on the aforementioned equipment status dataset; determining a second equipment sub-state based on the aforementioned equipment pressure dataset when the first equipment sub-state indicates that the air-using equipment is in a non-operating state; and determining that the air-using equipment status indicates that the air-using equipment is not operating when the second equipment sub-state indicates that the pressure change of the air-using equipment is less than a second preset threshold.

[0008] According to embodiments of this disclosure, the aforementioned air-using equipment includes at least one of an air compressor, a pantograph, a door, an air spring, and a brake cylinder; the aforementioned first equipment sub-state includes at least one of an air compressor operating state, a pantograph operating state, and a door operating state; the aforementioned second equipment sub-state includes at least one of an air spring pressure change and a brake cylinder pressure change.

[0009] According to embodiments of this disclosure, the aforementioned second total duct pressure dataset includes subsets of second total duct pressure data corresponding to each of the multiple carriages; determining the airtightness test result for the air supply system based on the second total duct pressure dataset within the second preset time period includes: determining the total duct pressure leakage amount of each carriage within the second preset time period based on each subset of the second total duct pressure data; and determining that the airtightness test result indicates that the air supply system has a leakage point at the carriage corresponding to the total duct pressure leakage amount when the total duct pressure leakage amount is determined to be greater than a third preset threshold.

[0010] According to an embodiment of this disclosure, the detection method for the above-mentioned rail vehicle air supply system further includes: when it is determined that the total air duct pressure leakage is greater than a third preset threshold, determining an early warning level based on the total air duct pressure leakage; and generating early warning information for the air supply system based on the early warning level.

[0011] Another aspect of this disclosure provides a detection system for a rail vehicle air supply system, comprising: a first determining module, configured to determine the vehicle operating condition of the vehicle under test based on a vehicle speed dataset and a first total air duct pressure dataset within a first preset time period, in response to determining that the vehicle under test is stationary; a second determining module, configured to determine the air-using equipment status of the vehicle under test based on an air-using equipment dataset of the vehicle under test within a second preset time period, wherein the vehicle operating condition indicates that the vehicle under test is in a stable operating condition; and a third determining module, configured to determine the air tightness detection result of the air supply system based on a second total air duct pressure dataset within the second preset time period, wherein the air-using equipment status indicates that the air-using equipment of the vehicle under test is not working.

[0012] Another aspect of this disclosure provides a train, including: a data acquisition module for acquiring vehicle speed datasets, a first main duct pressure dataset, a ventilation equipment dataset, and a second main duct pressure dataset; wherein the acquisition module is connected to a ground-based intelligent operation and maintenance system, and the acquisition module is used to transmit the vehicle speed dataset, the first main duct pressure dataset, the ventilation equipment dataset, and the second main duct pressure dataset to the ground-based intelligent operation and maintenance system, so that the ground-based intelligent operation and maintenance system can perform the above-described detection method for the rail vehicle air supply system.

[0013] Another aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the methods described above.

[0014] Another aspect of this disclosure provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the methods described above.

[0015] Another aspect of this disclosure provides a computer program product including computer-executable instructions that, when executed, implement the methods described above.

[0016] According to embodiments of this disclosure, by automatically determining the vehicle's stationary state, vehicle operating condition, and air supply equipment status, dynamic interference factors in the air tightness testing process are eliminated. This solves the problem that the air tightness test results are easily affected by vehicle operating conditions and air supply equipment, resulting in poor accuracy. Thus, when a controlled testing environment is met, the air tightness test of the air supply system can be automatically performed, improving the efficiency and real-time performance of the air tightness test of the air supply system. Attached Figure Description

[0017] The above and other objects, features and advantages of this disclosure will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0018] Figure 1 An exemplary system architecture for a detection method applicable to a rail vehicle air supply system, according to embodiments of this disclosure, is illustrated schematically.

[0019] Figure 2 A flowchart illustrating a detection method for a rail vehicle air supply system according to an embodiment of the present disclosure is shown.

[0020] Figure 3 A flowchart illustrating the determination of airtightness test results according to embodiments of the present disclosure is shown schematically.

[0021] Figure 4 A block diagram of a detection system for a rail vehicle air supply system according to an embodiment of the present disclosure is shown schematically.

[0022] Figure 5 A block diagram of a train according to an embodiment of the present disclosure is schematically shown; and

[0023] Figure 6 A block diagram of an electronic device suitable for implementing a detection method for a rail vehicle air supply system, according to an embodiment of the present disclosure, is shown schematically. Detailed Implementation

[0024] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0025] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0026] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0027] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0028] In the embodiments disclosed herein, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of data (e.g., including but not limited to user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0029] In the embodiments disclosed herein, user authorization or consent is obtained before acquiring or collecting user personal information.

[0030] Traditional methods for testing air supply systems involve manual inspection. Before testing, it's necessary to manually confirm the vehicle's stationary status, such as whether the vehicle load has changed and whether equipment like restrooms and air conditioners are operating. Then, the air supply system's airtightness is tested. During the airtightness test, the air compressor is manually operated to its maximum pressure, and the total air pressure drop over a preset time period is recorded. If the total air pressure drop is less than 30 kPa, the test result indicates good airtightness; if it's greater than 30 kPa, the test result indicates a significant leak. However, manual testing of air supply systems is not only inefficient but also fails to detect abnormalities in a timely manner when the testing intervals are long.

[0031] The embodiments of this disclosure provide a detection method for a rail vehicle air supply system, comprising: in response to determining that the vehicle to be tested is stationary, determining the vehicle operating condition of the vehicle to be tested based on a vehicle speed dataset and a first total air duct pressure dataset within a first preset time period; if the vehicle operating condition indicates that the vehicle to be tested is in a stable operating condition, determining the air-using equipment status of the vehicle to be tested based on an air-using equipment dataset of the vehicle to be tested within a second preset time period, wherein the second preset time period is after the first preset time period; if the air-using equipment status indicates that the air-using equipment of the vehicle to be tested is not working, determining the air tightness detection result for the air supply system based on a second total air duct pressure dataset within the second preset time period.

[0032] The embodiments of this disclosure monitor the operational status of the train set and the operating status of each ventilation device by using real-time onboard parameters and data from the rail vehicle. They automatically identify the stationary state of the vehicle, monitor changes in the main ventilation duct of the train, and then perform automated intelligent diagnosis of the air tightness of the main ventilation duct. This eliminates the need to rely on the experience of the testing personnel, enabling early warning of abnormal leakage in the main ventilation system and automated performance evaluation of the air tightness of the vehicle's ventilation system, thereby improving the efficiency and accuracy of air tightness diagnosis for the ventilation system.

[0033] Figure 1 This illustration schematically depicts an exemplary system architecture for a detection method applicable to a rail vehicle's air supply system, according to embodiments of this disclosure. It should be noted that... Figure 1 The examples shown are merely examples of system architectures that can be applied to the embodiments of this disclosure, in order to help those skilled in the art understand the technical content of this disclosure, but do not mean that the embodiments of this disclosure cannot be used in other devices, systems, environments or scenarios.

[0034] like Figure 1 As shown, the system architecture according to this embodiment may include a vehicle under test 101, a communication system 102, and a ground-based intelligent operation and maintenance system 103. The communication system 102 serves as a medium for providing a communication link between the vehicle under test 101 and the ground-based intelligent operation and maintenance system 103. The communication system 102 may include various connection types, such as wired and / or wireless communication links, etc.

[0035] The vehicle to be tested 101 collects data such as vehicle speed, first main air duct pressure, second main air duct pressure, and air-using equipment. For example, the main air duct pressure data and air-using equipment status data can be collected through the brake control unit, and vehicle speed data and air-using equipment pressure data can be collected through the network control unit.

[0036] After the vehicle to be tested 101 collects data, it can send the vehicle speed dataset, the total air duct pressure dataset, and the air-using equipment dataset to the ground intelligent operation and maintenance system 103 through the communication system 102.

[0037] The ground intelligent operation and maintenance system 103 can be a system that provides operation and maintenance services for the vehicle 101 to be inspected, such as performing inspection and fault diagnosis on the vehicle 101 to be inspected based on the data sent by the vehicle 101 to be inspected.

[0038] It should be noted that the detection method for the air supply system of the vehicle under test provided in this embodiment can generally be executed by the ground intelligent operation and maintenance system 103. Correspondingly, the detection system for the air supply system of the vehicle under test provided in this embodiment can generally be set up in the ground intelligent operation and maintenance system 103. The detection method for the air supply system of the vehicle under test provided in this embodiment can also be executed by a server or server cluster that is different from the ground intelligent operation and maintenance system 103 and can communicate with the vehicle under test 101 and / or the ground intelligent operation and maintenance system 103. Correspondingly, the detection system for the air supply system of the vehicle under test provided in this embodiment can also be set up in a server or server cluster that is different from the ground intelligent operation and maintenance system 103 and can communicate with the vehicle under test 101 and / or the ground intelligent operation and maintenance system 103. Alternatively, the detection method for the air supply system of the vehicle under test provided in this embodiment can also be executed by the vehicle under test 101. Correspondingly, the detection system for the air supply system of the vehicle under test provided in this embodiment can also be set up in the vehicle under test 101.

[0039] For example, the vehicle to be tested 101 sends the vehicle speed dataset, the first main duct pressure dataset, the air-using equipment dataset, and the second main duct pressure dataset to the ground intelligent operation and maintenance system 103 via the communication system 102. The ground intelligent operation and maintenance system 103, in response to the vehicle to be tested 101 being stationary, determines the vehicle operating condition of the vehicle to be tested 101 based on the vehicle speed dataset and the first main duct pressure dataset within a first preset time period. If the vehicle operating condition indicates that the vehicle to be tested 101 is in a stable operating condition, the system determines the air-using equipment status of the vehicle to be tested 101 based on the air-using equipment dataset of the railcar within a second preset time period, which is after the first preset time period. If the air-using equipment status indicates that the air-using equipment of the vehicle to be tested 101 is not working, the system determines the airtightness test result for the air supply system based on the second main duct pressure dataset within the second preset time period.

[0040] It should be understood that Figure 1 The number of vehicles to be tested, communication systems, and ground-based intelligent operation and maintenance systems shown is merely illustrative. Any number of vehicles to be tested, communication systems, and ground-based intelligent operation and maintenance systems can be included depending on implementation needs.

[0041] Figure 2 A flowchart illustrating a detection method for a rail vehicle air supply system according to an embodiment of the present disclosure is shown.

[0042] like Figure 2 As shown, the method includes operations S210~S230.

[0043] In operation S210, in response to determining that the vehicle to be tested is stationary, the vehicle operating condition of the vehicle to be tested is determined based on the vehicle speed dataset and the first total air duct pressure dataset of the vehicle to be tested within a first preset time period.

[0044] According to embodiments of this disclosure, the vehicle to be inspected can be a rail train, and the vehicle to be inspected can collect vehicle data and send the vehicle data to a ground-based intelligent operation and maintenance system. The ground-based intelligent operation and maintenance system can perform fault detection on the vehicle to be inspected based on the vehicle data. For example, the vehicle data may include vehicle speed data, total duct pressure data, and air-using equipment data, etc.

[0045] According to embodiments of this disclosure, a ground-based intelligent operation and maintenance system can determine whether a vehicle to be inspected is stationary based on vehicle speed data. If it is determined that the vehicle to be inspected is stationary within a first preset time period, the system can automatically execute a detection method for the air supply system to test its airtightness. For example, the first preset time period can be one minute.

[0046] According to the embodiments of this disclosure, when performing air tightness testing on the air supply system, even if the vehicle under test is stationary, the pressure of the main air duct may continue to change due to factors such as the air compressor being turned off, making it impossible to obtain accurate air tightness test results. Therefore, before performing air tightness testing, it is necessary to determine the vehicle's operating condition based on the vehicle speed dataset and the first main air duct pressure dataset, and then perform air tightness testing only when the vehicle is in a stable operating condition.

[0047] According to embodiments of this disclosure, the vehicle under test can be determined to be stationary within a first preset time period based on the vehicle speed dataset, and if the total duct pressure changes little within the first preset time period based on the first total duct pressure dataset, the vehicle under test is determined to be in a stable operating condition; otherwise, the vehicle under test is determined to be in an unstable operating condition, and the airtightness test is not continued.

[0048] In operation S220, when the vehicle condition characterization vehicle under test is in a stable condition, the air condition status of the vehicle under test is determined based on the air condition data set of the vehicle under test within the second preset time period. The second preset time period is after the first preset time period.

[0049] According to the embodiments of this disclosure, since the air supply equipment of the rail vehicle consumes compressed air during operation, the pressure of the main air duct will drop, which will lead to inaccurate air tightness test results. Therefore, if it is determined that the vehicle under test maintains a stable operating condition in the first preset period, the status of the air supply equipment of the vehicle under test can be further determined. Air tightness test is then performed when all the air supply equipment is in a non-working state.

[0050] According to embodiments of this disclosure, it can be determined from the data of the air-consuming equipment that the equipment is in a non-operating state during a second preset period. For example, it can be determined from the pressure data of the air-consuming equipment whether the pressure of the equipment has changed, or from the operating data of the air-consuming equipment whether the equipment is operating. For example, the second preset period can be 5 minutes.

[0051] According to embodiments of this disclosure, if it is determined that the air-using equipment is in a non-working state during a second preset time period, an air tightness test can be performed; otherwise, vehicle data, total duct pressure, and the status of the air-using equipment are continuously tested simultaneously until it is determined that the vehicle under test is in a stable operating condition and the air-using equipment is in a non-working state.

[0052] In operation S230, when the air supply equipment of the vehicle under test is not working, the air tightness test result for the air supply system is determined based on the second total air duct pressure dataset within the second preset time period.

[0053] According to embodiments of this disclosure, when determining the airtightness test result, the change of the total air duct pressure during the second preset time period can be determined based on the second total air duct pressure dataset of the vehicle under test during the second preset time period.

[0054] According to embodiments of this disclosure, if the total duct pressure changes significantly within a second preset time period, it can be determined that there is a leak in the air supply system; conversely, it can be determined that the air supply system is airtight.

[0055] According to embodiments of this disclosure, by automatically determining the vehicle's stationary state, vehicle operating condition, and air supply equipment status, dynamic interference factors in the air tightness testing process are eliminated. This solves the problem that the air tightness test results are easily affected by vehicle operating conditions and air supply equipment, resulting in poor accuracy. Thus, when a controlled testing environment is met, the air tightness test of the air supply system can be automatically performed, improving the efficiency and real-time performance of the air tightness test of the air supply system.

[0056] According to an embodiment of this disclosure, the vehicle operating condition of the vehicle to be tested is determined based on a vehicle speed dataset and a first total duct pressure dataset within a first preset time period, including: determining the maximum value and minimum value of the first total duct pressure from multiple first total duct pressure data in the first total duct pressure dataset; and determining that the vehicle to be tested is in a stable operating condition when the difference between the maximum value and the minimum value of the first total duct pressure is less than a first preset threshold and multiple vehicle speed data in the vehicle speed dataset are all zero.

[0057] According to embodiments of this disclosure, when determining whether a vehicle under test is in a stable operating condition, it is necessary to determine whether the vehicle under test is stationary and whether the total duct pressure is stable. Specifically, if the vehicle under test is stationary and the change in total duct pressure is less than a first preset threshold, the vehicle under test is determined to be in a stable operating condition.

[0058] According to the embodiments of this disclosure, when determining the change in total duct pressure, the maximum value and minimum value of the first total duct pressure within the first preset time period can be determined as the total duct pressure deviation based on the first total duct pressure dataset within the first preset time period, and the change in total duct pressure within the first preset time period can be determined based on the difference between the maximum value and the minimum value of the first total duct pressure.

[0059] According to embodiments of this disclosure, when determining the change in total duct pressure within a first preset time period, the difference between the first maximum and minimum total duct pressure can be compared with a first preset threshold to determine whether the change in total duct pressure is within a normal range. For example, the first preset threshold can be 10 kPa; if the difference is less than 10 kPa, the vehicle under test is determined to be in a stable operating condition.

[0060] According to embodiments of this disclosure, when determining whether a vehicle to be detected is stationary, it can be determined whether the speed of the vehicle to be detected is zero within a first preset time period based on a vehicle speed dataset. If multiple vehicle speed datasets are all zero, it can be determined that the vehicle to be detected is stationary within the first preset time period.

[0061] According to embodiments of this disclosure, by determining vehicle operating conditions based on vehicle speed datasets and first main duct pressure datasets, the accuracy and efficiency of determining vehicle operating conditions are improved, thereby improving the accuracy and efficiency of determining airtightness test results.

[0062] According to an embodiment of this disclosure, determining the air-use equipment status of a vehicle under test based on a dataset of air-use equipment of the vehicle under test within a second preset time period includes: determining a first equipment sub-state based on the equipment status dataset; determining a second equipment sub-state based on the equipment pressure dataset when the first equipment sub-state indicates that the air-use equipment is in a non-working state; and determining that the air-use equipment status indicates that the air-use equipment is not working when the second equipment sub-state indicates that the pressure change of the air-use equipment is less than a second preset threshold.

[0063] According to embodiments of this disclosure, the air-using equipment dataset includes an equipment status dataset and an equipment pressure dataset. Specifically, an air-using equipment may have a corresponding equipment status dataset but not an equipment pressure dataset, or it may have both corresponding equipment status datasets and equipment pressure datasets simultaneously.

[0064] According to embodiments of this disclosure, the equipment status dataset can directly characterize the working status of the air-using equipment. For example, 1 represents that the air-using equipment A is in a working state, and 0 represents that the air-using equipment A is in a non-working state.

[0065] According to embodiments of this disclosure, the equipment pressure dataset can characterize the pressure value of the air-using equipment during a first preset time period, for example, the pressure of air-using equipment B is 100 kPa.

[0066] According to the embodiments of this disclosure, it may be difficult to eliminate the influence of air-consuming equipment on the total duct pressure by relying solely on the equipment status dataset. For example, when the air compressor is just turned off, the equipment status dataset indicates that the air compressor is in an inactive state, but the pressure of the air compressor is still changing. Therefore, it is necessary to determine the pressure change of the air-consuming equipment in order to ensure that the air-consuming equipment is not working and to avoid the hidden air consumption of the air-consuming equipment causing a drop in the total duct pressure, which in turn affects the air tightness test results.

[0067] According to embodiments of this disclosure, in order to ensure that the air-using equipment does not consume air, after determining that the first equipment state indicates that the air-using equipment is in a non-working state, it is necessary to further determine that the air-using equipment is not working based on the equipment pressure data.

[0068] According to embodiments of this disclosure, when determining whether a ventilation device consumes air during a second preset time period, the difference between the maximum and minimum device pressure values ​​in the device pressure data set can be compared with a second preset threshold. For example, the second preset threshold can be 10 kPa; if the difference between the maximum and minimum device pressure values ​​is less than 10 kPa, it is determined that the ventilation device is not operating.

[0069] According to embodiments of this disclosure, by using the equipment status dataset and equipment pressure dataset of the air-using equipment, subsequent air tightness testing is only performed when it is ensured that the air-using equipment is not consuming air, thereby eliminating the impact of air consumption by the air-using equipment on the air tightness of the air supply system and improving the testing accuracy.

[0070] According to embodiments of this disclosure, the air-powered device includes at least one of an air compressor, a pantograph, a door, an air spring, and a brake cylinder; a first device sub-state includes at least one of an air compressor operating state, a pantograph operating state, and a door operating state; a second device sub-state includes at least one of an air spring pressure change and a brake cylinder pressure change.

[0071] According to embodiments of this disclosure, the equipment status dataset may include air compressor operating status data, pantograph operating status data, and door operating status data. The equipment pressure dataset may include air spring pressure data and brake cylinder pressure data.

[0072] According to embodiments of this disclosure, the air compressor's operating state may include the air compressor being operational and the air compressor not being operational; the pantograph's operating state may include the pantograph lowering and raising; and the door's operating state may include the door not moving and the door moving. When the air compressor is not operational, the pantograph is lowered, and the door is not moving, a first equipment sub-state is determined to indicate that the air-using equipment is in an inoperable state.

[0073] According to embodiments of this disclosure, when the change in air spring pressure and the change in brake cylinder pressure are less than 10 kPa, it is determined that the air-cooled equipment is not working.

[0074] According to embodiments of this disclosure, by determining the working status and pressure changes of air-using equipment such as air compressors, pantographs, doors, air springs, and brake cylinders, the impact of air consumption by air-using equipment on air tightness test results can be reduced, thereby improving the accuracy of air tightness test results.

[0075] According to an embodiment of this disclosure, the air tightness test result for the air supply system is determined based on the second total duct pressure data set within a second preset time period, including: determining the total duct pressure leakage of each car within a second preset time period based on each subset of the second total duct pressure data; and determining that the air tightness test result indicates that there is a leak point in the air supply system at the car corresponding to the total duct pressure leakage when the total duct pressure leakage is determined to be greater than a third preset threshold.

[0076] According to embodiments of this disclosure, if it is determined that the air-consuming equipment does not consume air during a second preset time period, the air tightness of the air supply system can be detected based on the second total duct pressure data during the second preset time period.

[0077] According to embodiments of this disclosure, the second main duct pressure dataset includes subsets of second main duct pressure data corresponding to each of the multiple carriages. During airtightness testing, the airtightness of the air supply system can be determined based on the subsets of second main duct pressure data from the carriages.

[0078] According to embodiments of this disclosure, when determining airtightness, the first second total duct pressure data in the second total duct pressure data subset can be determined as the initial value of the total duct pressure, and the difference between the other second total duct pressure data in the second total duct pressure data subset and the initial value of the total duct pressure can be determined as the total duct pressure leakage.

[0079] According to an embodiment of this disclosure, the third preset threshold can be 20 kPa. If there is a total duct pressure leakage greater than 20 kPa among multiple total duct pressure leakage amounts, the air tightness test result is determined to be that there is a leakage point in the air supply system in the carriage.

[0080] According to embodiments of this disclosure, the air tightness of the air supply system of each car is tested based on a subset of the second main air duct pressure data of each car, thereby improving the precision of the air tightness test and the accuracy of the air tightness test results.

[0081] Figure 3 A flowchart illustrating the determination of airtightness test results according to an embodiment of the present disclosure is shown schematically.

[0082] like Figure 3 As shown, determining the airtightness test results includes operations S310 to S350.

[0083] In operation S310, it is determined whether the total wind pressure deviation is less than a first preset threshold. If the total wind pressure deviation is less than the first preset threshold, operation S320 is executed; otherwise, operation S310 is executed.

[0084] In operation S320, determine whether the vehicle speed data is zero. If the vehicle speed data is zero, execute operation S330; otherwise, execute operation S310.

[0085] In operation S330, determine whether the air-consuming equipment is in a non-operating state. If the air-consuming equipment is in a non-operating state, execute operation S340; otherwise, execute operation S330.

[0086] In operation S340, it is determined whether the pressure change of the air-consuming equipment is less than a second preset threshold. If the pressure change of the air-consuming equipment is less than the second preset threshold, operation S350 is executed; otherwise, operation S330 is executed.

[0087] In operation S350, the air tightness test results for the air supply system are determined based on the second total duct pressure dataset within the second preset time period.

[0088] According to an embodiment of this disclosure, the detection system for the air supply system of a rail vehicle further includes: determining an early warning level based on the total air duct pressure leakage when the total air duct pressure leakage is determined to be greater than a third preset threshold; and generating early warning information for the air supply system based on the early warning level.

[0089] According to the embodiments of this disclosure, if the total duct pressure leakage is greater than a third preset threshold, it can be determined that there is a leak in the air supply system. At this time, an early warning message can be generated to prompt maintenance personnel to inspect the air supply system.

[0090] According to embodiments of this disclosure, the severity of a leak in the air supply system can be characterized by the total duct pressure leakage, and a warning level can be determined based on the total duct pressure leakage. For example, the greater the total duct pressure leakage, the higher the warning level.

[0091] For example, a third preset threshold can be set, including multiple third preset sub-thresholds such as 20 kPa, 30 kPa, and 50 kPa. When the total duct pressure leakage is greater than 20 kPa but less than 30 kPa, the warning level is determined to be low; when the total duct pressure leakage is greater than 30 kPa but less than 50 kPa, the warning level is determined to be medium; and when the total duct pressure leakage is greater than 50 kPa, the warning level is determined to be high.

[0092] According to embodiments of this disclosure, corresponding warning information can be generated based on the warning level. Specifically, the warning information may include the carriage with a leak, the total duct pressure leakage of the carriage, and the warning level, etc.

[0093] According to embodiments of this disclosure, by determining the warning level based on the total duct pressure leakage and generating corresponding warning information, a graded warning system is achieved, significantly improving the maintenance efficiency of the rail vehicle air supply system.

[0094] Figure 4 A block diagram of a detection system for a rail vehicle air supply system according to an embodiment of the present disclosure is shown schematically.

[0095] like Figure 4 As shown, the detection system 400 for the air supply system of a rail vehicle includes a first determining module 410, a second determining module 420, and a third determining module 430.

[0096] The first determining module 410 is configured to, in response to determining that the vehicle to be detected is stationary, determine the vehicle operating condition of the vehicle to be detected based on the vehicle speed dataset and the first total air duct pressure dataset within a first preset time period. In one embodiment, the first determining module 410 may be used to perform the operation S210 described above, which will not be repeated here.

[0097] The second determining module 420 is used to determine the air-consuming equipment status of the vehicle under test based on the air-consuming equipment dataset of the vehicle under test within a second preset time period, provided that the vehicle operating condition characterization indicates that the vehicle under test is in a stable operating condition. The second preset time period is after the first preset time period. In one embodiment, the second determining module 420 can be used to perform the operation S420 described above, which will not be repeated here.

[0098] The third determining module 430 is used to determine the air tightness test result for the air supply system based on the second total duct pressure dataset within a second preset time period when the air supply equipment status characterization of the vehicle under test is not working. In one embodiment, the third determining module 430 can be used to perform the operation S230 described above, which will not be repeated here.

[0099] According to embodiments of this disclosure, the first determining module 410 includes a pressure determining submodule and an operating condition determining submodule.

[0100] The pressure determination submodule is used to determine the maximum and minimum values ​​of the first total duct pressure from multiple first total duct pressure data in the first total duct pressure dataset.

[0101] The operating condition determination submodule is used to determine that the vehicle under test is in a stable operating condition when the difference between the maximum value of the first total duct pressure and the minimum value of the first total duct pressure is less than a first preset threshold and multiple vehicle speed data in the vehicle speed dataset are all zero.

[0102] According to embodiments of this disclosure, the air-using equipment dataset includes an equipment status dataset and an equipment pressure dataset. The second determination module 420 includes a first status determination submodule, a second status determination submodule, and an equipment status determination submodule.

[0103] The first state determination submodule is used to determine the first device substate based on the device state dataset.

[0104] The second state determination submodule is used to determine the second equipment substate based on the equipment pressure dataset when the first equipment substate characterizes the air equipment as being in an inactive state.

[0105] The equipment status determination submodule is used to determine that the air-using equipment is not working when the pressure change of the second equipment sub-status characterizing the air-using equipment is less than a second preset threshold.

[0106] According to embodiments of this disclosure, the air-powered device includes at least one of an air compressor, a pantograph, a door, an air spring, and a brake cylinder; a first device sub-state includes at least one of an air compressor operating state, a pantograph operating state, and a door operating state; a second device sub-state includes at least one of an air spring pressure change and a brake cylinder pressure change.

[0107] According to embodiments of this disclosure, the second main duct pressure dataset includes subsets of second main duct pressure data corresponding to each of the multiple carriages. The third determination module 430 includes a leakage determination module and a result determination submodule.

[0108] The leakage determination submodule is used to determine the total duct pressure leakage of each car within a second preset time period based on each subset of second total duct pressure data.

[0109] The result determination submodule is used to determine, when the total duct pressure leakage is greater than the third preset threshold, that the air tightness test result indicates that there is a leak point in the air supply system at the compartment corresponding to the total duct pressure leakage.

[0110] According to embodiments of this disclosure, the detection system 400 for the air supply system of a rail vehicle further includes a level determination module and an early warning generation module.

[0111] The level determination module is used to determine the warning level based on the total duct pressure leakage when the total duct pressure leakage exceeds the third preset threshold.

[0112] The early warning generation module is used to generate early warning information for the air supply system based on the early warning level.

[0113] Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure, or at least part of the functions of any one or more of them, can be implemented in one module. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be implemented by dividing them into multiple modules. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be at least partially implemented as hardware circuitry, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a System-on-Chip, a System-on-a-Substrate, a System-on-Package, an Application-Specific Integrated Circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be at least partially implemented as computer program modules, which, when run, can perform corresponding functions.

[0114] For example, any plurality of the first determining module 410, the second determining module 420, and the third determining module 430 may be combined into one module / unit / subunit, or any one of these modules / units / subunits may be split into multiple modules / units / subunits. Alternatively, at least part of the functionality of one or more of these modules / units / subunits may be combined with at least part of the functionality of other modules / units / subunits and implemented in one module / unit / subunit. According to embodiments of this disclosure, at least one of the first determining module 410, the second determining module 420, and the third determining module 430 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuitry, or implemented in any one of the three methods of software, hardware, and firmware, or in a suitable combination of any of these. Alternatively, at least one of the first determining module 410, the second determining module 420, and the third determining module 430 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0115] It should be noted that the detection system part of the rail vehicle air supply system in the embodiments of this disclosure corresponds to the detection method part of the rail vehicle air supply system in the embodiments of this disclosure. For a detailed description of the detection system part of the rail vehicle air supply system, please refer to the detection method part of the rail vehicle air supply system, which will not be repeated here.

[0116] Figure 5 A block diagram of a train according to an embodiment of the present disclosure is shown schematically.

[0117] like Figure 5 As shown, the train 500 includes a data acquisition module 510, which is used to acquire vehicle speed dataset, first main duct pressure dataset, air-using equipment dataset, and second main duct pressure dataset, and send the vehicle speed dataset, first main duct pressure dataset, air-using equipment dataset, and second main duct pressure dataset to the ground intelligent operation and maintenance system so that the ground intelligent operation and maintenance system can execute the detection method of the rail vehicle air supply system.

[0118] The acquisition module 510 may include a brake control unit 511 and a network control unit 512. The brake control unit 511 can be used to acquire main air duct pressure data, air spring pressure data, air compressor working status data and brake cylinder pressure data. The network control unit 512 can be used to acquire vehicle speed data, pantograph working status data and door working status data.

[0119] Figure 6 A block diagram of an electronic device suitable for implementing a detection method for a rail vehicle air supply system, according to an embodiment of the present disclosure, is shown schematically. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0120] like Figure 6 As shown, an electronic device 600 according to an embodiment of this disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage portion 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this disclosure.

[0121] RAM 603 stores various programs and data required for the operation of electronic device 600. Processor 601, ROM 602, and RAM 603 are interconnected via bus 604. Processor 601 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 602 and / or RAM 603. It should be noted that programs may also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in one or more memories.

[0122] According to embodiments of this disclosure, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to a bus 604. The electronic device 600 may also include one or more of the following components connected to the input / output (I / O) interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 610 as needed so that computer programs read from it can be installed into the storage section 608 as needed.

[0123] According to embodiments of this disclosure, the method flow according to embodiments of this disclosure can be implemented as a computer software program. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by processor 601, it performs the functions defined in the system of embodiments of this disclosure. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0124] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0125] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0126] For example, according to embodiments of this disclosure, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0127] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods provided in the embodiments of this disclosure. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the detection method for the air supply system of a rail vehicle provided in the embodiments of this disclosure.

[0128] When the computer program is executed by the processor 601, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0129] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and / or installed from the removable medium 611. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0130] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0131] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways, even if such combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of this disclosure may be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0132] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A method for testing a ventilation system for rail vehicles, comprising: In response to determining that the vehicle to be tested is stationary, the vehicle operating condition of the vehicle to be tested is determined based on the vehicle speed dataset and the first total air duct pressure dataset of the vehicle to be tested within a first preset time period. When the vehicle operating condition characterization indicates that the vehicle under test is in a stable operating condition, the air-use equipment status of the vehicle under test is determined based on the air-use equipment dataset of the vehicle under test within a second preset time period, wherein the second preset time period is after the first preset time period. When the air supply equipment status indicates that the air supply equipment of the vehicle under test is not working, the air tightness test result for the air supply system is determined based on the second total duct pressure dataset within the second preset time period.

2. The detection method according to claim 1, characterized in that, The step of determining the vehicle operating condition of the vehicle under test based on the vehicle speed dataset and the first total duct pressure dataset within a first preset time period includes: From multiple first total duct pressure data in the first total duct pressure dataset, determine the maximum and minimum values ​​of the first total duct pressure; If the difference between the maximum and minimum values ​​of the first total duct pressure is less than a first preset threshold, and multiple vehicle speed data in the vehicle speed dataset are all zero, then the vehicle to be tested is determined to be in a stable operating condition.

3. The detection method according to claim 1 or 2, characterized in that, The air-using equipment dataset includes an equipment status dataset and an equipment pressure dataset; The step of determining the status of the ventilation equipment of the vehicle under test based on the ventilation equipment dataset of the vehicle under test within the second preset time period includes: Based on the device status dataset, determine the first device sub-state; When the first equipment sub-state characterization air equipment is in an inactive state, the second equipment sub-state is determined based on the equipment pressure dataset; If the pressure change of the air-using equipment is less than a second preset threshold in the second equipment sub-state, it is determined that the air-using equipment is not working.

4. The detection method according to claim 3, wherein the air-using equipment includes at least one of an air compressor, a pantograph, a door, an air spring, and a brake cylinder; the first equipment sub-state includes at least one of an air compressor working state, a pantograph working state, and a door working state; the second equipment sub-state includes at least one of an air spring pressure change and a brake cylinder pressure change.

5. The detection method according to any one of claims 1 to 4, characterized in that, The second main duct pressure dataset includes subsets of the second main duct pressure data corresponding to each of the multiple carriages; The step of determining the airtightness test result of the air supply system based on the second total duct pressure data set within the second preset time period includes: Based on each subset of the second total duct pressure data, determine the total duct pressure leakage of each of the carriages within the second preset time period; If the total duct pressure leakage is determined to be greater than a third preset threshold, the air tightness test result indicates that there is a leak point in the air supply system at the compartment corresponding to the total duct pressure leakage.

6. The method according to claim 5, characterized in that, The detection method further includes: If the total duct pressure leakage is determined to be greater than a third preset threshold, the warning level is determined based on the total duct pressure leakage. Based on the warning level, a warning message is generated for the air supply system.

7. A detection system for a rail vehicle air supply system, comprising: The first determining module is used to determine the vehicle operating condition of the vehicle under test in response to determining that the vehicle under test is stationary, based on the vehicle speed dataset and the first total air duct pressure dataset of the vehicle under test within a first preset time period. The second determining module is used to determine the air-use equipment status of the vehicle under test based on the air-use equipment dataset of the vehicle under test within a second preset time period, when the vehicle operating condition characterization indicates that the vehicle under test is in a stable operating condition. The second preset time period is after the first preset time period. The third determining module is used to determine the air tightness test result for the air supply system based on the second total duct pressure dataset within the second preset time period when the air supply equipment status indicates that the air supply equipment of the vehicle under test is not working.

8. A train, comprising: The data acquisition module is used to collect vehicle speed datasets, first main duct pressure datasets, air-using equipment datasets, and second main duct pressure datasets. The acquisition module is connected to the ground intelligent operation and maintenance system. The acquisition module is used to transmit the vehicle speed dataset, the first main air duct pressure dataset, the air-using equipment dataset, and the second main air duct pressure dataset to the ground intelligent operation and maintenance system so that the ground intelligent operation and maintenance system can execute the detection method of the rail vehicle air supply system as described in any one of claims 1 to 6.

9. An electronic device, comprising: One or more processors; Memory, used to store one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 6.

10. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 6.

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