Railway vehicle data analysis method, railway vehicle data analysis device, and railway vehicle data analysis computer program

Abstract

Aspects relate to providing rail vehicle data analysis techniques for facilitating detection of rail vehicle stopping accuracy degradation and providing insight into potential causes of the stopping accuracy degradation for a particular rail vehicle. The rail vehicle data analysis method includes obtaining a set of rail vehicle operating data, identifying a set of rail vehicle stopping accuracy values, determining a set of anomalous stopping accuracy values, defining a set of analysis variables that may affect the rail vehicle stopping accuracy, defining a set of analysis functions for identifying the set of analysis variables, extracting data corresponding to a time frame including the set of analysis variables, identifying a set of feature values ​​indicative of potential causes of the anomalous set of stopping accuracy values, and generating a set of output data indicative of the set of feature values ​​for each rail vehicle.

Description

[Technical Field]

[0001] The present disclosure relates to a railway vehicle data analysis method, a railway vehicle data analysis device, and a railway vehicle data analysis computer program. [Background technology]

[0002] In recent years, as railway systems have become more sophisticated, the importance of reliably monitoring, collecting, and transmitting information related to the operation of rail cars in a train configuration has also increased. Analysis of operational information collected from rail car systems can provide valuable insights into operational efficiency and safety.

[0003] Conventionally, techniques have been developed for analyzing railway vehicle operation data to detect abnormalities. As an example of railway vehicle data anomaly technology, Japanese Patent Application Laid-Open No. 2006-117190 (Patent Document 1) discloses that "an object of the present invention is to provide an ATC data analysis system that can automatically perform analytical processing such as railway vehicle anomaly detection using ATC data. An ATC analysis device (21) is disclosed that uses a database (200) that stores ATC data including ATC chart data transmitted from an ATC device mounted on a railway vehicle. The ATC analysis device 21 uses the ATC data to run data analysis software that performs analytical functions including a railway vehicle anomaly detection function, a deceleration measurement function, and a slip / skid detection function." [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-117190 Summary of the Invention [Problem to be solved by the invention]

[0005] Stopping accuracy is one of the key indicators of operation efficiency in automatic train operation (ATO) systems. Typically, in ATO systems, train stopping control is performed manually to stop the train at a designated location, such as a boarding / alighting area at a railway station. Poor stopping accuracy may cause the train to overshoot or undershoot the designated stopping location, which may disrupt passenger movement and lead to safety concerns.

[0006] Train stopping accuracy can be affected by any of a number of factors, including brake performance, friction, weather conditions, voltage, and train approach speed, etc. Therefore, to maintain operational efficiency and safety, it is desirable to detect stopping accuracy degradation and diagnose potential causes of the detected degradation.

[0007] Patent Document 1 discloses a technique for automatically detecting the presence of an abnormality based on the train speed and the use of emergency interrupts by analyzing automatic train control (ATC) data, but does not discuss or provide a technique for detecting a deterioration in stopping accuracy or a technique for diagnosing the potential cause of any detected deterioration in stopping accuracy. As a result, train operating companies must use a trial-and-error approach to estimate the cause of the deterioration in stopping accuracy of their railway vehicle systems.

[0008] Accordingly, it is an object of the present disclosure to provide rail vehicle data analysis techniques to facilitate detection of rail vehicle stopping accuracy degradation and provide insight into potential causes of the degradation in stopping accuracy for a particular rail vehicle. [Means for solving the problem]

[0009] One representative example of the present disclosure relates to a method for analyzing rail vehicle data, the method including: obtaining a set of rail vehicle operating data related to operation of a rail vehicle; identifying a first subset of rail vehicle operating data from the set of rail vehicle operating data using a set of stopping accuracy conditions, the first subset of rail vehicle operating data including a set of stopping accuracy values ​​for the rail vehicle; determining a second subset of rail vehicle operating data from the first subset of rail vehicle operating data including an anomalous set of stopping accuracy values ​​using a predetermined stopping accuracy threshold; defining a set of analysis variables associated with operating events that may affect the stopping accuracy of the rail vehicle; defining a set of analysis functions for identifying the set of analysis variables in the second subset of rail vehicle operating data; extracting a third subset of rail vehicle operating data from the second subset of rail vehicle operating data corresponding to a time frame including the set of analysis variables using the set of analysis functions; identifying a set of feature values ​​from the third subset of rail vehicle operating data that are indicative of potential causes of the anomalous set of stopping accuracy values ​​using statistical analysis techniques; and generating a set of output data indicative of the set of feature values ​​associated with the set of rail vehicle operating data. [Effects of the Invention]

[0010] The present disclosure provides rail vehicle data analysis techniques to facilitate detection of rail vehicle stopping accuracy degradation and provide insight into potential causes of the degradation of stopping accuracy for a particular rail vehicle.

[0011] Problems, configurations, and advantages other than those mentioned above will become apparent from the following description of embodiments for carrying out the present invention. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 illustrates an exemplary computing architecture for implementing embodiments of the present disclosure. [Figure 2]FIG. 2 is a diagram illustrating an exemplary hardware configuration of a railcar data analysis system according to an embodiment of the present disclosure. [Figure 3] FIG. 3 is a flowchart illustrating a method for analyzing rail vehicle data according to an embodiment of the present disclosure. [Figure 4] FIG. 4 is a diagram illustrating an analysis variable table for storing a set of analysis variables according to an embodiment of the present disclosure. [Figure 5] FIG. 5 is a diagram illustrating an analytic function table for storing a set of analytic functions according to an embodiment of the present disclosure. [Figure 6] FIG. 6 is a diagram illustrating a composite analytic function table for storing a set of composite analytic functions according to an embodiment of the present disclosure. [Figure 7] FIG. 7 is a diagram illustrating a stopping precision condition table for storing a set of stopping precision conditions according to an embodiment of the present disclosure. [Figure 8] FIG. 8 is a diagram illustrating the configuration of a first subset of rail vehicle operating data according to an embodiment of the present disclosure. [Figure 9] FIG. 9 is a diagram illustrating the configuration of a second subset of rail vehicle operating data according to an embodiment of the present disclosure. [Figure 10] FIG. 10 is a diagram illustrating the configuration of a set of output data according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0013]

[0023] Herein, embodiments of the present invention will be described with reference to the drawings. It should be noted that the embodiments described herein are not intended to limit the present invention in accordance with the claims, and it should be understood that each element and combination of elements described with respect to the embodiments is not strictly necessary to practice aspects of the present invention.

[0014] Various aspects are disclosed in the following description and related drawings. Alternative aspects may be devised without departing from the scope of the present disclosure. Additionally, well-known elements of the present disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of the present disclosure.

[0015] The words "exemplary" and / or "example" are used herein to mean "serving as an example, instance, or illustrative example." Any aspect described herein as "exemplary" and / or "example" is not necessarily to be construed as preferred or advantageous over other aspects. Likewise, the term "aspects of the present disclosure" does not require that all aspects of the present disclosure include the discussed feature, advantage, or characteristic of operation.

[0016] Further, many aspects are described in terms of sequences of actions to be performed by, for example, elements of a computing device. It will be appreciated that the various actions described herein can be performed by specific circuitry (e.g., an application-specific integrated circuit (ASIC)), by program instructions executed by one or more processors, or a combination of both. In addition, the sequences of actions described herein can be considered to be embodied as a whole in any form of computer-readable storage medium storing a corresponding set of computer instructions that, when executed, can cause an associated processor to perform the functions described herein. Thus, various aspects of the present disclosure may be embodied in many different forms, all of which are contemplated to be within the subject matter of the claims.

[0017] Detailed descriptions of embodiments of the present disclosure are described herein with reference to the drawings.

[0018] Referring now to the drawings, Figure 1 illustrates a schematic block diagram of a computer system 100, according to an embodiment, for implementing various embodiments of the present disclosure. The mechanisms and apparatus of various embodiments disclosed herein apply equally to any suitable computing system. The major components of computer system 100 include one or more processors 102, memory 104, terminal interface 112, storage interface 113, I / O (input / output) device interface 114, and network interface 115, all of which are communicatively coupled, directly or indirectly, for inter-component communication via memory bus 106, I / O bus 108, bus interface unit 109, and I / O bus interface unit 110.

[0019] Computer system 100 may include one or more general-purpose programmable central processing units (CPUs) 102A and 102B, generally referred to herein as processors 102. In embodiments, computer system 100 may include multiple processors, although in certain embodiments, computer system 100 may alternatively be a single CPU system. Each processor 102 executes instructions stored in memory 104 and may include one or more levels of on-board cache.

[0020] In embodiments, memory 104 may include random-access semiconductor memory, storage devices, or storage media (either volatile or nonvolatile) for storing or encoding data and programs. In certain embodiments, memory 104 represents the entire virtual memory of computer system 100 and may also include virtual memory of other computer systems coupled to computer system 100 or connected via a network. While memory 104 can conceptually be viewed as a single monolithic entity, in other embodiments, memory 104 is a more complex configuration, such as a hierarchy of caches and other memory elements. For example, memory may exist in multiple levels of caches, which may be further divided by function, whereby one cache holds instructions and another cache holds non-instruction data used by the processor. Memory may also be distributed and associated with different CPUs or sets of CPUs, as is known in any of a variety of so-called non-uniform memory access (NUMA) computer architectures.

[0021] Memory 104 may store all or a portion of the various programs, modules, and data structures for handling data transfers described herein. For example, memory 104 may store railcar data analysis application 150. In an embodiment, railcar data analysis application 150 may include instructions or statements that are executed on processor 102 or interpreted by processor 102 to perform functions as described further below. In certain embodiments, railcar data analysis application 150 is implemented in hardware via semiconductor devices, chips, logic gates, circuits, circuit cards, and / or other physical hardware devices instead of or in addition to a processor-based system. In embodiments, railcar data analysis application 150 may include data in addition to instructions or statements. In certain embodiments, cameras, sensors, or other data input devices (not shown) may be provided in direct communication with bus interface unit 109, processor 102, or other hardware of computer system 100. In such a configuration, the need for processor 102 to access memory 104 and railcar data analysis application 150 may be reduced.

[0022] Computer system 100 may include a bus interface unit 109 that handles communication between processor 102, memory 104, display system 124, and I / O bus interface unit 110. I / O bus interface unit 110 may be coupled to I / O bus 108 to transfer data to and from various I / O units. I / O bus interface unit 110 communicates with multiple I / O interface units 112, 113, 114, and 115, also known as I / O processors (IOPs) or I / O adapters (IOAs), via I / O bus 108. Display system 124 may include a display controller, display memory, or both. The display controller may provide video, audio, or both types of data to display device 126. Additionally, computer system 100 may include one or more sensors or other devices configured to collect and provide data to processor 102. By way of example, computer system 100 may include biometric sensors (e.g., collecting heart rate data, stress level data), environmental sensors (e.g., collecting humidity data, temperature data, pressure data), or motion sensors (e.g., collecting acceleration data, movement data), etc. Other types of sensors are possible. Display memory may be dedicated memory for buffering video data. Display system 124 may be coupled to a display device 126, such as a standalone display screen, a computer monitor, a television, or the display of a tablet or handheld device. In one embodiment, display device 126 may include one or more speakers for rendering audio. Alternatively, one or more speakers for rendering audio may be coupled to the I / O interface unit. In an alternative embodiment, one or more of the functions provided by display system 124 may be incorporated into an integrated circuit that also includes processor 102. Additionally, one or more of the functions provided by bus interface unit 109 may be incorporated into an integrated circuit that also includes processor 102.

[0023] The I / O interface unit supports communication with various storage and I / O devices. For example, the terminal interface unit 112 supports the connection of one or more user I / O devices 116, which may include user output devices (such as a video display device, speakers, and / or a television receiver) and user input devices (such as a keyboard, mouse, keypad, touchpad, trackball, buttons, light pen, or other pointing device). A user may use a user interface to manipulate the user input devices to provide input data and commands to the user I / O devices 116 and the computer system 100, and may also receive output data via the user output devices. For example, the user interface may be presented via the user I / O devices 116, such as displayed on a display device, played through speakers, or printed by a printer.

[0024] Storage interface 113 supports the connection of one or more disk drives or direct access storage devices 117 (typically rotating magnetic disk drive storage devices, but may alternatively be other storage devices, including arrays of disk drives or solid-state drives such as flash memory configured to appear as a single mass storage device to a host computer). In some embodiments, storage device 117 may be implemented by any type of secondary storage device. The contents of memory 104, or any portion thereof, may be stored in storage device 117 and retrieved from storage device 117 as needed. I / O device interface 114 provides an interface to any of a variety of other I / O devices or other types of devices, such as printers or fax machines. Network interface 115 provides one or more communication paths from computer system 100 to other digital devices and computer systems; these communication paths may include, for example, one or more networks 130.

[0025] 1 illustrates a particular bus structure providing direct communication paths between processor 102, memory 104, bus interface 109, display system 124, and I / O bus interface unit 110. In alternative embodiments, computer system 100 may include different buses or communication paths that may be arranged in any of a variety of forms, such as hierarchical, star, or web configurations, multiple hierarchical buses, parallel and redundant paths, or point-to-point links in any other suitable type of configuration. Furthermore, while I / O bus interface unit 110 and I / O bus 108 are shown as single respective units, computer system 100 may actually include multiple I / O bus interface units 110 and / or multiple I / O buses 108. While multiple I / O interface units are shown isolating I / O bus 108 from the various communication paths running to the various I / O devices, in other embodiments, some or all of the I / O devices are directly connected to one or more system I / O buses.

[0026] In various embodiments, computer system 100 is a multi-user mainframe computer system, a single-user system, or a server computer, or similar device with little or no direct user interface, but which receives requests from other computer systems (clients). In other embodiments, computer system 100 may be implemented as a desktop computer, a portable computer, a laptop or notebook computer, a tablet computer, a pocket computer, a telephone, a smartphone, or any other suitable type of electronic device.

[0027] Next, an exemplary hardware configuration of a railway vehicle data analysis system according to an embodiment of the present disclosure will be described with reference to FIG.

[0028] 2 is a diagram illustrating an exemplary hardware configuration of a railcar data analysis system 200 according to an embodiment of the present disclosure. The railcar data analysis system 200 relates to an information processing system configured to collect a set of railcar operating data related to the operation of a railcar, identify data for analysis related to the stopping accuracy of the railcar, set analysis parameters, and perform an analysis to identify potential causes of the deterioration of the stopping accuracy of the railcar.

[0029] 2 , a railcar data analysis system 200 according to an embodiment of the present disclosure includes a railcar 210, a user terminal 220, a communication network 230, and a railcar data analysis device 240. In the railcar data analysis system 200, the railcar 210, the user terminal 220, and the railcar data analysis device 240 may be communicatively connected via the communication network 230. Here, the communication network 230 may include a local area network (LAN) connection, the Internet, a wide area network (WAN) connection, or a metropolitan area network (MAN) connection, or the like.

[0030] In an embodiment, rail vehicle 210 may include one or more rail cars, such as a train, mechanically coupled or connected to travel on tracks extending along a route. Alternatively, the rail cars may not be mechanically coupled, but may communicate with one another so that the cars coordinate their movements and a fleet of cars moves along a route in unison. Rail vehicle 210 may be used in operations described as freight rail, passenger rail, high-speed rail, commuter rail, rail transit, subway, light rail, streetcar, tramway, or rail-tram vehicle. In an embodiment, rail vehicle 210 may be configured to record a set of rail vehicle operating data characterizing its operation and performance. This rail vehicle operating data may be transmitted periodically or in real time via communications network 230 to user terminal 220 and rail vehicle data analyzer 240, or may be stored in local storage on rail vehicle 210 for manual collection once a run is completed.

[0031] User terminal 220 is a device usable by a user (e.g., a client) of railcar data analyzer 240. In an embodiment, user terminal 220 may be used to request analysis of a set of railcar operating data recorded for railcar 210 by railcar data analyzer 240 and to determine the results of this analysis. Additionally, in an embodiment, a user terminal may be used to allow a user to define a set of analysis variables and / or a set of analysis functions for analyzing the railcar operating data. By way of example, user terminal 220 may be implemented using a personal computer, a tablet computer, a smartphone, or other computing device.

[0032] Railcar data analyzer 240 is a device configured to analyze a set of railcar operating data to detect deterioration in stopping accuracy of railcar 210 and identify potential causes of said deterioration in stopping accuracy. In an embodiment, railcar data analyzer 240 may be implemented using computer system 100 shown in FIG. 1 as part of a distributed computing architecture. For example, the functionality of railcar data analyzer 240 may be implemented using one or more computing devices (e.g., computer system 100) that comprise a cloud infrastructure.

[0033] 2, the railcar data analysis device 240 may include a data acquisition unit 242, an analyzed data extraction unit 244, an analysis management unit 246, and an analysis unit 248. In an embodiment, the data acquisition unit 242, the analyzed data extraction unit 244, the analysis management unit 246, and the analysis unit 248 may be implemented as software modules constituting the railcar data analysis application 150 stored in the memory 104 of the computer system 100 shown in FIG. 1. In this manner, the functions of the data acquisition unit 242, the analyzed data extraction unit 244, the analysis management unit 246, and the analysis unit 248 may be executed by the processor 102 of the computer system 100, thereby realizing the techniques of the present disclosure.

[0034] Data acquisition unit 242 is a functional unit for acquiring a set of rail vehicle operating data for rail vehicle 210. In an embodiment, data acquisition unit 242 may acquire the set of rail vehicle operating data from user terminal 220. For example, a user of user terminal 220 may upload the set of rail vehicle operating data to rail vehicle data analyzer 240 via a graphical user interface provided by data acquisition unit 242. In an embodiment, data acquisition unit 242 may send a data acquisition request directly to rail vehicle 210 to acquire the set of rail vehicle operating data. Alternatively, in certain embodiments, rail vehicle 210 may be configured to automatically upload the set of rail vehicle operating data to data acquisition unit 242. This automatic data upload may be performed at regular time intervals, such as after the occurrence of a particular event (e.g., the departure or arrival of rail vehicle 210 from a particular location) or in response to the collection of a certain amount of data.

[0035] The analysis data extraction unit 244 is a functional unit that extracts a set of analysis data to be analyzed from the set of rail vehicle operating data acquired by the data acquisition unit 242. In an embodiment, the analysis data extraction unit 244 may identify a first subset of rail vehicle operating data including a set of stopping accuracy values ​​for the rail vehicle 210 from the set of rail vehicle operating data using a set of stopping accuracy conditions, and may determine a second subset of rail vehicle operating data including an abnormal set of stopping accuracy values ​​from the first subset of rail vehicle operating data using a predetermined stopping accuracy threshold. The second subset of rail vehicle operating data may be used as the set of analysis data.

[0036] The analysis management unit 246 is a functional unit for setting parameters to facilitate analysis of the set of analysis data extracted by the analysis data extraction unit 244. In an embodiment, the analysis management unit may define a set of analysis variables related to operating events that may affect the stopping accuracy of the rail vehicle 210 and define a set of analysis functions for identifying the set of analysis variables within the second subset of the rail vehicle operating data. In an embodiment, the set of analysis variables and the set of analysis functions may be specified based on user input (e.g., user input received from a user of the user terminal 220).

[0037] Analysis unit 248 is a functional unit for performing analysis on the set of analysis data (e.g., the second subset of rail vehicle operating data) using parameters (e.g., analysis variables and analysis functions) set by analysis management unit 246. In an embodiment, analysis unit 248 may extract a third subset of rail vehicle operating data corresponding to a time frame including the set of analysis variables from the second subset of rail vehicle operating data using the set of analysis functions, identify a set of feature values ​​from the third subset of rail vehicle operating data using statistical analysis techniques that are indicative of potential causes of a set of abnormal stopping accuracy values, and generate a set of output data indicative of the set of feature values ​​in association with the set of rail vehicle operating data. Analysis unit 248 may then provide the set of output data to user terminal 220.

[0038] The railcar data analysis device 240 shown in FIG. 2 can provide a railcar data analysis technique to facilitate detection of railcar stopping accuracy degradation and provide insight into potential causes of the degradation in stopping accuracy for a particular railcar.

[0039] Next, a railway vehicle data analysis method according to an embodiment of the present disclosure will be described with reference to FIG.

[0040] 3 illustrates a railcar data analysis method 300 according to an embodiment of the present disclosure. The railcar data analysis method 300 is a method for analyzing a set of railcar operating data to detect deterioration in railcar stopping accuracy and identify potential causes of the deterioration in stopping accuracy. The railcar data analysis method 300 may be performed by various functional units of the railcar data analysis apparatus 240 shown in FIG. 2.

[0041] First, in step S305, the data acquisition unit 242 acquires a set of rail vehicle operating data related to the operation of a rail vehicle. The set of rail vehicle operating data may be a structured collection of information characterizing the operation of a rail vehicle. For example, the set of rail vehicle operating data may include information regarding speed, acceleration, brake usage, electrical parameters, stopping accuracy, signals, departure and arrival times, route, and other parameters related to the operation of the rail vehicle. In a particular embodiment, the set of rail vehicle operating data may include a set of time-series operating records including at least a speed value of the rail vehicle at a specific time while traveling along the route, a stopping accuracy value of the rail vehicle at a specific time while traveling along the route, and a next station tag where the rail vehicle is scheduled to stop on the route. In a particular embodiment, the set of rail vehicle operating data may be ATC (automatic train control) data.

[0042] In an embodiment, data acquisition unit 242 may acquire the set of rail vehicle operating data from user terminal 220. For example, a user of user terminal 220 shown in FIG. 2 may upload the set of rail vehicle operating data to rail vehicle data analyzer 240 via a graphical user interface provided by data acquisition unit 242. In an embodiment, data acquisition unit 242 may send a data acquisition request directly to rail vehicle 210 to acquire the set of rail vehicle operating data. Alternatively, in certain embodiments, rail vehicle 210 may be configured to automatically upload the set of rail vehicle operating data to data acquisition unit 242. This automatic data upload can be performed at regular time intervals, such as after the occurrence of a particular event (e.g., the departure or arrival of rail vehicle 210 from a particular location) or in response to the collection of a certain amount of data.

[0043] Next, in step S310, the analysis data extraction unit 244 identifies a first subset of rail vehicle operating data from the set of rail vehicle operating data obtained in step S305, the first subset including a set of stopping accuracy values ​​for the rail vehicle. As described herein, the set of stopping accuracy values ​​is one or more numerical values ​​indicating the accuracy with which the rail vehicle stops (e.g., the speed becomes zero) relative to a specified stopping position. In an embodiment, the set of stopping accuracy values ​​may include distance values ​​indicating how far the rail vehicle stopped from the specified stopping position. A negative distance value may indicate that the rail vehicle stopped before reaching the specified stopping position (e.g., the rail vehicle undershot the stopping position), a zero value may indicate that the rail vehicle stopped exactly at the specified stopping position, and a positive distance value may indicate that the rail vehicle exceeded the specified stopping position (e.g., the rail vehicle overshot the stopping position). In an embodiment, the analysis data extraction unit 244 may identify one or more rail vehicle stopping accuracy values ​​for each of a plurality of predetermined stops for each rail vehicle along the route and aggregate the identified stopping accuracy values ​​as a first subset of rail vehicle operating data.

[0044] Generally, stopping accuracy values ​​of a rail vehicle may be continuously calculated in real time while the rail vehicle is operating and stored as a time-series record. As an example, records of stopping accuracy values ​​may be calculated and recorded at regular time intervals of approximately several hundred milliseconds. Furthermore, aspects of the present disclosure relate to the recognition that calculated stopping accuracy values ​​may lag behind the actual movement of the rail vehicle due to the time required for sensor data collection and stopping accuracy calculation processing. Therefore, calculation and generation of stopping accuracy records may continue for several time intervals after the rail vehicle actually stops at the designated stopping position. As a result, the stopping accuracy record that most accurately represents the distance between the rail vehicle and the designated stopping position may not be the stopping accuracy record corresponding to the time the rail vehicle actually stops, but rather the stopping accuracy record for a few hours after the rail vehicle stops.

[0045] In view of the above, therefore, aspects of the present disclosure relate to using a set of stopping accuracy conditions to identify a set of stopping accuracy values ​​for a rail vehicle, where the set of stopping accuracy values ​​may include one or more stopping accuracy values ​​that most accurately indicate the distance of the rail vehicle relative to a particular stopping position. The stopping accuracy conditions refer to requirements that can be used to identify a stopping accuracy record that most accurately indicates the stopping accuracy of the rail vehicle relative to a particular stopping position. Examples of stopping accuracy conditions are described below with respect to FIG. 7, and therefore further description thereof will be omitted here.

[0046] In an embodiment, the analytical data extraction unit 244 may identify a set of rail vehicle stopping accuracy values ​​by identifying, from the set of time-series driving records, a first time-series driving record in which the rail vehicle speed value is zero, a time-series driving record in which the immediately preceding time-series driving record has a non-zero rail vehicle speed value, a time-series driving record in which the immediately following time-critical driving record has a zero rail vehicle speed value, and a time-series driving record in which the next station tag changes in the immediately following time-series driving record. In this manner, the analytical data extraction unit 244 may identify stopping accuracy values ​​corresponding to times when the rail vehicle travels at a constant speed, comes to a stop, remains stationary, and has not yet started moving toward the next station on its route. The analytical data extraction unit 244 may then extract, as a first subset of rail vehicle driving data, the next station tag from the first time-series driving record and the stopping accuracy value from a second time-series driving record that is a predetermined number of records after the first time-series driving record. The predetermined number of records may be determined based on the time-series driving record calculation time (i.e., the time it takes for the stopping accuracy value to be calculated and the corresponding record to be created). In this way, the analysis data extraction unit 244 can identify the stopping accuracy value corresponding to the time after the generation of the stopping accuracy value record for a specific specified stopping position is completed, thereby making it possible to take into account the time delay in record generation and select the stopping accuracy record that most accurately indicates the stopping accuracy of the railway vehicle for a specific stopping position.

[0047] Next, in step S315, the analysis data extraction unit 244 determines a second subset of rail vehicle operating data from the first subset of rail vehicle operating data identified in step S310, the second subset including a set of anomalous stopping accuracy values. Here, the set of anomalous stopping accuracy values ​​may include stopping accuracy values ​​that indicate a potential error in the rail vehicle's stopping capability. The set of anomalous stopping accuracy values ​​may be determined using a predetermined stopping accuracy threshold. In an embodiment, the stopping accuracy threshold may indicate a range of acceptable stopping accuracy values ​​(e.g., −100 cm to +100 cm). Thus, the analysis data extraction unit 244 may determine, from the first subset of rail vehicle operating data, time-series operating records having stopping accuracy values ​​that do not fall within the range defined by the stopping accuracy threshold as the second subset of rail vehicle operating data. Note that in addition to the set of anomalous stopping accuracy values, the second subset of rail vehicle operating data also includes operating parameters for the rail vehicle (such as time-series speed information and interruption usage information). In an embodiment, the second subset of rail vehicle operating data may be transmitted to a nested space for analysis, which may be a logical space configured for temporary storage and analysis of rail vehicle operating data.

[0048] Next, in step S320, the analysis management unit 246 may define a set of analysis variables associated with operational events that may affect the stopping accuracy of the rail vehicle. The set of analysis variables refers to specific parameters within the set of rail vehicle operating data that are associated with operational events that may affect the stopping accuracy of the rail vehicle. Here, an operational event refers to any state, incident, action, or situation that occurs during the operation of the rail vehicle. For example, the analysis variables may include a slip variable associated with an operational event in which the wheels of the rail vehicle slip on the rails, a speed variable associated with the speed of the rail vehicle, and an acceleration variable associated with the acceleration of the rail vehicle. The set of analysis variables may be used by an analysis function, described below, to identify the presence of operational events that may affect the stopping accuracy of the rail vehicle.

[0049] In an embodiment, the analysis management unit 246 may define the set of analysis variables based on input from a user (e.g., a user of the user terminal 220 shown in FIG. 2 ). For example, a user may define the set of analysis variables based on variables that are believed to have an effect on the stopping accuracy of a rail vehicle. In a particular embodiment, the analysis management unit 246 may define the set of analysis variables to include all or a portion of the parameters defined in the rail vehicle operating data. Examples of sets of analysis variables are described below with respect to FIG. 4 , and therefore, a description thereof will not be repeated here.

[0050] Next, in step S325, the analysis management unit 246 may define a set of analysis functions. The set of analysis functions refers to logical functions for identifying the set of analysis variables defined in step S320 within the second subset of the rail vehicle operating data. As an example, the set of analysis functions may include a slip function for identifying a slip variable within the second subset of the rail vehicle operating data or a speed function for identifying data corresponding to a time during which the rail vehicle travels at a certain speed. In a particular embodiment, the analysis management unit 246 may define a composite analysis function including a first analysis function for identifying a first analysis variable of the set of analysis variables within the second subset of the rail vehicle operating data and a second analysis function for identifying a second analysis variable of the set of analysis variables within the second subset of the rail vehicle operating data. Examples of analysis functions and sets of composite analysis functions are described below with reference to FIGS. 5 and 6, and therefore, a description thereof will not be repeated here.

[0051] Next, in step S330, analysis unit 248 uses the set of analysis functions defined in step S325 to extract from the second subset of rail vehicle operating data a third subset of rail vehicle operating data corresponding to a predetermined time frame that includes the set of analysis variables. As an example, analysis unit 248 may extract a portion of the second subset of rail vehicle operating data that corresponds to a time frame from 30 seconds before to 10 seconds after the occurrence of an operating event that corresponds to a particular analysis variable. In this way, by determining railway vehicle operation data (second subset) that includes abnormal stopping accuracy values ​​and extracting a portion of this railway vehicle operation data (third subset) that corresponds to a period during which a specific operating event that may affect stopping accuracy occurred, it is possible to facilitate analysis of the potential causes of the abnormal stopping accuracy values.

[0052] Next, in step S335, analysis unit 248 uses statistical analysis techniques to identify a set of feature values ​​from the third subset of rail vehicle operating data that are indicative of potential causes of the set of anomalous stopping accuracy values. In embodiments, analysis unit 248 may use statistical analysis techniques to identify parameters associated with values ​​classified as statistical outliers. In embodiments, analysis unit 248 may identify maximum values ​​of one or more predetermined parameters from within the third subset of rail vehicle operating data as the set of feature values. As an example, analysis unit 248 may identify maximum speed values ​​and maximum brake voltage values ​​for rail vehicles in the third subset of rail vehicle operating data as the set of feature values. In embodiments, this analysis may be performed within a nested space.

[0053] Next, in step S340, analysis unit 248 may generate a set of output data indicating sets of feature values ​​associated with the sets of rail vehicle operating data. In an embodiment, the analysis unit may visually highlight the feature values ​​for each set of rail vehicle operating data in the set of output data. Furthermore, in a particular embodiment, the analysis unit may determine potential causes of the set of abnormal stopping accuracy values ​​based on the set of feature values ​​and include the potential causes in the set of output data. The analysis unit may output the set of output data to user terminal 220 via communication network 230. An example set of output data is described below with respect to FIG. 12 , and therefore, a description thereof will not be repeated here.

[0054] According to the railcar data analysis method 300 shown in FIG. 3, a set of stopping accuracy conditions can be used to account for time delays in record generation and to identify a first subset of railcar operating data that includes stopping accuracy records that most accurately indicate the railcar's stopping accuracy for a particular stopping position. Furthermore, an analytical function can be used to analyze a second subset of railcar operating data that has an abnormal stopping accuracy value, thereby extracting a third subset of railcar operating data that includes the occurrence of an operating event that may affect stopping accuracy. Furthermore, by analyzing the third subset of railcar operating data with statistical analysis techniques, feature values ​​that indicate potential causes of the abnormal stopping accuracy value can be determined. In this manner, it is possible to provide rail vehicle data analysis techniques to facilitate detection of rail vehicle stopping accuracy degradation and provide insight into potential causes of stopping accuracy degradation for a particular rail vehicle.

[0055] Next, with reference to FIG. 4, a set of analysis variables according to an embodiment of the present disclosure will be described.

[0056] 4 is a diagram illustrating an analysis variable table 400 for storing a set of analysis variables according to an embodiment of the present disclosure. As shown in FIG. 4, the analysis variable table 400 can include a variable number 402, a variable name 404, a variable source address 406, a variable destination address 408, a transformation data 410, a note 412, and a creator 414. The variable number 402 indicates a number that serves as a unique identifier for each analysis variable in the analysis variable table 400 .

[0057] Variable name 404 indicates the name of a particular analysis variable. As described herein, an analysis variable according to an embodiment of the present disclosure refers to a particular parameter within a set of rail vehicle operating data that is related to an operational event that may affect the stopping accuracy of a rail vehicle. For example, as shown in FIG. 4 , the analysis variables may include a slip variable related to the rail vehicle's wheels sliding on the rails, a speed variable related to the rail vehicle's speed, an acceleration variable related to the rail vehicle's acceleration, an overstop variable related to the rail vehicle overshooting a stopping position, an emergency interrupt variable related to the rail vehicle's use of an emergency interrupt, and a voltage variable related to the voltage supplied to the rail vehicle (e.g., the voltage supplied to the rail vehicle's interrupt). As described herein, the analysis variables specified by variable names 404 may be used by analysis functions, described below, to identify the presence of operational events that may have an impact on the stopping accuracy of the rail vehicle. In an embodiment, a user (e.g., a user of user terminal 220 shown in FIG. 2 ) may specify analysis variables in analysis variable table 400 based on variables believed to have an impact on the stopping accuracy of the rail vehicle. In an embodiment, analysis variable table 400 may be structured to include all or a portion of the parameters defined in the rail vehicle operating data.

[0058] Variable source address 406 is information that defines the source location of a particular analysis variable within a data file that contains a set of rail vehicle operating data. For example, if the data file that contains the set of rail vehicle operating data is formatted as a proprietary data file (e.g., a PCF file, a CSV file, or other tabular data format), variable source address 406 may specify the row and column of a table within the data file that contains the particular analysis variable. Variable destination address 408 is information that defines the destination location of a particular analysis variable to be stored within a data file containing a set of rail vehicle operating data after any base conversion (such as binary to decimal) or other preprocessing is complete.

[0059] Conversion data 410 is information specifying the base conversion to be applied to a particular analysis variable. For example, the conversion data may indicate a binary to decimal numeric conversion, a binary to hexadecimal numeric conversion, or a hexadecimal to decimal numeric conversion.

[0060] Notes 412 are information to indicate the purpose of a particular analysis variable. Creator 414 indicates the user who defined a particular analysis variable.

[0061] Next, a set of analytical functions according to an embodiment of the present disclosure will be described with reference to FIG.

[0062] 5 is a diagram illustrating an analysis function table 500 for storing a set of analysis functions according to an embodiment of the present disclosure. As shown in FIG. 5, the analysis function table 500 may include a function number 502, a function name 504, a function variable 506, a function operator 508, a bit change value (before) 510, a bit change value (after) 512, a range value (lower limit) 514, a range value (upper limit) 516, a note 518, and a creator 520.

[0063] The function number 502 indicates a number that serves as a unique identifier for each analytical function in the analytical function table 500 .

[0064] Function name 504 indicates the name of a particular analysis function. As described herein, the set of analysis functions is a logical function for identifying a set of analysis variables in analysis variable table 400 shown in FIG. 4 within the rail vehicle operating data (the second subset of the rail vehicle operating data). By way of example, as shown in FIG. 5, the set of analysis functions may include a slip function for identifying a slip variable within the second subset of the rail vehicle operating data, or a speed function for identifying data corresponding to the time the rail vehicle traveled at a certain speed. Multiple analysis functions may be defined for a particular analysis variable. For example, analysis variable table 400 may include a first speed function for identifying rail vehicles traveling at speeds of 60 kilometers per hour or more and a second speed function for identifying rail vehicles traveling at speeds between 20 and 60 kilometers per hour. In an embodiment, a user (e.g., a user of the user terminal 220 shown in FIG. 2) can specify an analytical function in the analytical function table 500 based on a variable (driving event) that they want to identify within a set of vehicle driving data.

[0065] Function variables 506 represent analysis variables that are operated on (i.e., determined) by a particular analysis function. By way of example, function variables 506 may include slip, speed, or emergency brake application.

[0066] The function operator 508 indicates a state to be identified with respect to a particular analysis variable. For example, as shown in Figure 5, the function operator 508 may include "change," "greater than," "less than," or "between," etc. As an example, a "slip function" with a function operator of "change" may be configured to identify any change in a binary bit that indicates the presence or absence of a slip on a railcar.

[0067] Bit change value (before) 510 indicates the initial state of a particular bit corresponding to a particular function variable 506 before the change, and bit change value (after) 512 indicates the final state of a particular bit corresponding to a particular function variable 506 after the change. Bit change value (before) 510 and bit change value (after) 512 can be used to monitor changes in a binary bit that indicates, for example, the presence or absence of slippage for a rail car.

[0068] Range value (lower limit) 514 indicates the lower limit of the numeric range of a particular bit corresponding to a particular function variable 506, and range value (upper limit) 516 indicates the upper limit of the numeric range of a particular bit corresponding to a particular function variable 506. Range value (lower limit) 514 and range value (upper limit) 516 can be used to monitor rail values ​​associated with function variable 506 that meet a particular numeric range, such as rail cars with a speed of 60 kilometers per hour or more or rail cars with a speed of 20 to 60 kilometers per hour.

[0069] Notes 518 are information to indicate the purpose of a particular parsing function. Creator 520 indicates the user who defined a particular parsing function.

[0070] Next, with reference to FIG. 6, a set of composite analytic functions according to an embodiment of the present disclosure will be described.

[0071] 6 is a diagram illustrating a composite analysis function table 600 for storing a set of composite analysis functions according to an embodiment of the present disclosure. As shown in FIG. 6, the composite analysis function table 600 may include a composite analysis function name 602, a composite analysis function logic 604, a note 606, and a creator 608.

[0072] As described herein, in certain embodiments, in addition to the set of analytical functions described above with reference to Figure 5, a set of composite analytical functions may be used to identify a set of analytical variables within the rail vehicle operating data. For example, the composite analytical function may be defined to include a first analytical function for identifying a first analytical variable of the set of analytical variables within a second subset of the rail vehicle operating data and a second analytical function for identifying a second analytical variable of the set of analytical variables within the rail vehicle operating data. The composite analytical function may be used to identify more detailed operating events within the set of rail vehicle operating data.

[0073] The composite analysis function name 602 indicates the name of a particular composite analysis function. For example, the composite analysis function name 602 may include, for example, "high speed slip" or "high speed and overstop."

[0074] Composite analytic function logic 604 represents logic for implementing a particular composite analytic function. In an embodiment, composite analytic function logic 604 may be specified in terms of the individual analytic functions it includes. For example, composite analytic function logic 604 may include a "slip function and speed function (60+)." This "high speed slip" function may be used to identify slip events for railcars traveling above 60 kilometers per hour. As another example, a "high speed and overstop" function defined by a "speed function (60+) and overstop" may be used to identify overstop events for railcars traveling above 60 kilometers per hour.

[0075] Notes 606 are information to indicate the purpose of a particular composite analysis function. Creator 608 indicates the user who defined a particular composite analytic function.

[0076] Next, a set of stopping accuracy conditions according to an embodiment of the present disclosure will be described with reference to FIG.

[0077] FIG. 7 is a diagram illustrating a stopping accuracy condition table 700 for storing a set of stopping accuracy conditions according to an embodiment of the present disclosure. As described herein, aspects of the present disclosure relate to using a set of stopping accuracy conditions to identify a set of stopping accuracy values ​​for a rail vehicle that most accurately indicates the distance of the rail vehicle relative to a particular stopping position, taking into account delays in generating time stopping accuracy records. Thus, the stopping accuracy condition table 700 illustrated in FIG. 7 illustrates an example of a stopping accuracy condition that can be used to identify a set of stopping accuracy values. It should be noted that the stopping accuracy conditions illustrated in FIG. 7 are merely examples, and the stopping accuracy conditions used to identify a set of stopping accuracy values ​​for a rail vehicle are not particularly limited herein, as any stopping accuracy condition or logic that can determine a set of stopping accuracy values ​​that reliably indicates the distance of the rail vehicle relative to a particular stopping position can be utilized.

[0078] As shown in FIG. 7 , the stopping accuracy condition table 700 may include a stopping accuracy condition 715 for identifying a particular record n ​​from a set of time-series operation records to be included in a first subset of rail vehicle operation data that includes a set of stopping accuracy values, and a recording action 720 for indicating information to be recorded when a particular record n ​​that satisfies the stopping accuracy condition 715 is identified.

[0079] In an embodiment, the stopping accuracy condition 715 may include a first condition specifying that the rail vehicle's speed value is zero in a particular first time-series driving record n, a second condition specifying that the rail vehicle's speed value is not zero in the previous time-series driving record n-1, a third condition specifying that the rail vehicle's speed value is zero in the immediately following time-critical driving record n+1, and a fourth condition specifying that the next station tag in the immediately following time-series driving record n+1 is not identical to the next station tag in the first time-series driving record n. By identifying a first time-series driving record n ​​that satisfies these conditions, it is possible to identify a stopping accuracy value corresponding to a time when the rail vehicle came to a stop after traveling at a certain speed, remained stationary, and had not yet begun driving toward the next station on its route (e.g., the last time-series driving record before the rail vehicle began driving toward the next station on its route).

[0080] Once a first time-series driving record n ​​that satisfies the stopping accuracy condition 715 is obtained, an action specified by record action 720 may be executed. In an embodiment, record action 720 may specify that the first time-series driving record n ​​and a stopping accuracy value from a second time-series driving record that is a predetermined number of records x after the first time-series driving record are extracted and recorded as a first subset of rail vehicle driving data. The value of x may be determined based on an average delay time in calculating and generating time-series driving records. As an example, if the stopping accuracy calculation takes 400 milliseconds and the time-series driving records are generated at 100 millisecond intervals, the value of x may be specified as “4.” As a result, the time-series driving record that is four records after the first time-series driving record n ​​(corresponding to the time 400 milliseconds after the train reached a stop and the stopping accuracy calculation ended) is identified as the second time-series driving record. In this way, it is possible to identify the stopping accuracy value corresponding to the time after the generation of the stopping accuracy value record for a specific specified stopping position has been completed, making it possible to take into account the time delay in record generation and select the stopping accuracy record that most accurately indicates the stopping accuracy of the railway vehicle for a specific stopping position.

[0081] Referring now to FIG. 8, a first subset of rail vehicle operating data according to an embodiment of the present disclosure will be described.

[0082] 8 is a diagram illustrating the configuration of a first subset of rail vehicle operating data 800 according to an embodiment of the present disclosure. As described herein, the first subset of rail vehicle operating data 800 is a subset of rail vehicle operating data that includes at least a set of stopping accuracy values. As shown in FIG. 8, the first subset of rail vehicle operating data 800 may include a record number 802, a rail car number 804, a data file name 806, a record time 808, a stopping accuracy value 810, and a station code 812.

[0083] Record number 802 is a number that uniquely identifies a particular data record within the first subset of rail vehicle operation data 800 . Railcar number 804 is a number that uniquely identifies a particular railcar.

[0084] The data file name 806 is a file name for identifying a data file containing railcar operation data for a particular railcar. Record time 808 indicates the date and time that a particular data file was recorded.

[0085] Stopping accuracy values ​​810 indicate stopping accuracy values ​​identified from each set of rail vehicle operating data (e.g., each data file). As shown in Figure 8, the set of stopping accuracy values ​​810 may include normal stopping accuracy values ​​as well as abnormal stopping accuracy values ​​(shown in bold in Figure 8) that do not meet a predetermined stopping accuracy threshold.

[0086] Station code 812 indicates the station code that corresponds to the identified set of stop precision values ​​810. That is, referring to Figure 8, each of the stop precision values ​​810 was identified for the station code "Tokyo."

[0087] Referring now to FIG. 9, a second subset of rail vehicle operating data will be described according to an embodiment of the present disclosure.

[0088] 9 is a diagram illustrating the configuration of a second subset of rail vehicle operating data 900 according to an embodiment of the present disclosure. As described herein, the second subset of rail vehicle operating data 900 is a subset of the first subset of rail vehicle operating data that includes at least a set of anomalous stopping accuracy values. As shown in FIG. 9, the second subset of rail vehicle operating data 900 may include a record number 902, a data file name 904, and a stopping accuracy value 906.

[0089] Record number 902 is a number that uniquely identifies a particular data record within the second subset of rail vehicle operation data 900 .

[0090] The data file name 904 is a file name for identifying a data file containing railcar operation data for a particular railcar.

[0091] Stopping accuracy values ​​906 indicate a set of anomalous stopping accuracy values ​​determined from the first subset of rail vehicle operating data. As described herein, in an embodiment, the set of anomalous stopping accuracy values ​​may be stopping accuracy values ​​in the first subset of rail vehicle operating data that do not fall within a range of acceptable stopping accuracy values ​​specified by a stopping accuracy threshold. As an example, if the stopping accuracy threshold specifies a range of -100 centimeters to 100 centimeters, stopping accuracy values ​​of 150, 300, -200, 350, 230, and 400 may be identified as anomalous stopping accuracy values.

[0092] Next, with reference to FIG. 10, a set of output data according to an embodiment of the present disclosure will be described.

[0093] 10 is a diagram illustrating the configuration of a set of output data 1000 according to an embodiment of the present disclosure. As described herein, the set of output data 1000 is a set of data that includes a set of feature values ​​indicative of potential causes of a set of anomalous stopping accuracy values ​​along with a set of railcar operation data. As shown in FIG. 10, the set of output data 1000 may include a record number 1002, a railcar number 1004, a data file name 1006, a time 1008, a first feature value 1010, and a second feature value 1012.

[0094] Record number 1002 is a number that uniquely identifies a particular data record within the set of output data 1000 .

[0095] The railcar number 1004 is a number that uniquely identifies a particular railcar.

[0096] The data file name 1006 is a file name for identifying a data file containing railcar operation data for a particular railcar. Time 1008 indicates the date and time that a particular data file was recorded.

[0097] The first feature value 1010 and the second feature value 1012 are values ​​corresponding to specific parameters related to potential causes of the set of anomalous stopping accuracy values. In embodiments, the first feature value 1010 and the second feature value 1012 may be maximum values ​​of the specific parameters present in the set of rail vehicle operating data. In embodiments, the first feature value 1010 and the second feature value 1012 may be identified using statistical analysis techniques to identify parameters associated with values ​​classified as statistical outliers. As an example, the first feature value 1010 may be a maximum speed value of a rail vehicle in the third subset of rail vehicle operating data, and the second feature value 1012 may be a maximum brake voltage value of a rail vehicle in the third subset of rail vehicle operating data.

[0098] In embodiments, one or more feature values ​​in the set of output data may be visually highlighted. For example, in certain embodiments, feature values ​​that do not meet the specified normal driving range threshold for the corresponding parameter may be bolded, highlighted, displayed in a different color, or otherwise visually highlighted. By way of example, with reference to FIG. 10 , voltage values ​​of “10 volts” and “20 volts” do not meet the normal driving range threshold of “80 volts to 300 volts” and may be visually highlighted in bold font. Similarly, a speed value of “1 kilometer per hour” does not meet the normal driving range threshold of “30 kilometers per hour to 80 kilometers per hour” and may also be visually highlighted in bold font.

[0099] Further, in certain embodiments, analysis unit 248 may be configured to analyze the set of feature values ​​to determine potential causes of the abnormal stopping accuracy values ​​to which a particular feature value corresponds and include the potential causes in the set of input data. In embodiments, determining the potential causes may include using a lookup table that indicates potential causes of poor stopping accuracy for a given feature value. As an example, using the lookup table, analysis unit 248 may determine that a feature value for a speed parameter below a threshold in the normal driving range is associated with "excessive deceleration during interruption distance," which is a potential cause of poor stopping accuracy. This determined potential cause 1014 may be added to the set of output data, such as in the form of associated metadata or a callout indicating the corresponding feature value.

[0100] As described herein, aspects of the present disclosure relate to rail vehicle data analysis techniques for facilitating detection of deterioration in stopping accuracy of a rail vehicle by analyzing stopping accuracy values ​​of the rail vehicle. However, as described herein, due to the time required for sensor data collection and stopping accuracy calculation processing, the calculated stopping accuracy value may lag relative to the actual movement of the rail vehicle, and therefore the calculation and generation of the stopping accuracy record may continue for several time intervals after the rail vehicle actually stops at the designated stopping position. As a result, the stopping accuracy record that most accurately represents the distance between the rail vehicle and the designated stopping position is not the stopping accuracy record that corresponds to the time the rail vehicle actually stopped, making it difficult to ascertain a reliable stopping accuracy value for stopping accuracy evaluation.

[0101] In view of the above, therefore, aspects of the present disclosure relate to using a set of stopping accuracy conditions to identify a set of stopping accuracy values ​​for a rail vehicle. The use of the set of stopping accuracy conditions allows for time delays in record generation and for identifying a first subset of rail vehicle operating data that includes stopping accuracy records that most accurately indicate the rail vehicle's stopping accuracy for a particular stopping position.

[0102] Furthermore, by using the set of analytical functions to analyze a second subset of rail vehicle operating data having an abnormal stopping accuracy value identified from the first subset of rail vehicle operating data, it is possible to extract a third subset of rail vehicle operating data including occurrences of operating events that may affect the stopping accuracy of the rail vehicle. These operating events may include, for example, emergency brake application, slip events, or speeds that exceed the normal operating range. In this way, it is possible to pinpoint the portion of the rail vehicle operating data in which the operating events that resulted in the deterioration of stopping accuracy occurred.

[0103] Furthermore, by analyzing the third subset of rail vehicle operating data with statistical analysis techniques, feature values ​​indicative of potential causes of the abnormal stopping accuracy values ​​can be determined. These feature values ​​may correspond to parameters associated with the poor stopping accuracy, such as abnormal speed or voltage values. Based on these feature values, potential causes of the poor stopping accuracy of the rail vehicle can be identified.

[0104] Thus, embodiments of the present disclosure can provide railcar data analysis techniques to facilitate detection of railcar stopping accuracy degradation and provide insight into potential causes of the degradation of stopping accuracy for a particular railcar.

[0105] As described herein, the present disclosure relates to the following embodiments:

[0106] (Aspect 1) A railway vehicle data analysis method, comprising: obtaining a set of rail vehicle operating data relating to the operation of the rail vehicle; identifying a first subset of rail vehicle operating data from the set of rail vehicle operating data using the set of stopping accuracy conditions, the first subset including a set of rail vehicle stopping accuracy values; determining, from the first subset of the rail vehicle operating data, a second subset of the rail vehicle operating data that includes an anomalous set of stopping accuracy values ​​using a predetermined stopping accuracy threshold; defining a set of analysis variables related to operational events that may affect stopping accuracy of the rail vehicle; defining a set of analytic functions to identify a set of analytic variables within the second subset of rail vehicle operating data; extracting, from the second subset of the rail vehicle operating data using the set of analytic functions, a third subset of the rail vehicle operating data corresponding to a time frame that includes the set of analytic variables; using statistical analysis techniques to identify a set of feature values ​​from the third subset of rail vehicle operating data that are indicative of potential causes of the set of anomalous stopping accuracy values; generating a set of output data indicative of a set of feature values ​​associated with the set of rail vehicle operating data; A railway vehicle data analysis method, including:

[0107] (Aspect 2) The set of rail vehicle operation data includes a set of time-series operation records, and the set of time-series operation records includes at least: a speed value of the rail vehicle at a particular time while traveling along the route; a stopping accuracy value of the rail vehicle at a particular time while traveling along the route; a next station tag indicating a subsequent station at which the rail vehicle is scheduled to stop on the route; 2. The railway vehicle data analysis method according to claim 1, comprising:

[0108] (Aspect 3) Identifying the first subset of rail vehicle operating data includes: identifying a first time-series driving record from the set of time-series driving records, wherein in the first time-series driving record: The speed value of the railcar is zero, The speed value of the railcar is greater than zero in the previous time-series operation record, In the immediately following time-critical driving record, the speed value of the railcar is zero, The next station tag is different from the next station tag of the immediately preceding time-series operation record. To identify and extracting, as a first subset of the railway vehicle operation data, a next station tag from a first time-series operation record and a stopping accuracy value from a second time-series operation record that is a predetermined number of records after the first time-series operation record; 3. The railway vehicle data analysis method according to claim 2, comprising:

[0109] (Aspect 4) 4. The railcar data analysis method according to aspect 3, wherein the predetermined number of records after the first time-series operation record is based on a time-series record calculation time.

[0110] (Aspect 5) A method for analyzing rail vehicle data according to any one of aspects 1 to 4, wherein the set of analysis variables is selected from the group consisting of slip, speed, acceleration, overstop, and use of emergency interrupt.

[0111] (Aspect 6) Defining a set of analytical functions is defining a composite analytical function including a first analytical function for identifying a first analytical variable of the set of analytical variables within the second subset of the rail vehicle operating data and a second analytical function for identifying a second analytical variable of the set of analytical variables within the second subset of the rail vehicle operating data; 6. The railway vehicle data analysis method according to any one of aspects 1 to 5, further comprising:

[0112] (Aspect 7) 7. The railway vehicle data analysis method according to any one of aspects 1 to 6, further comprising visually highlighting feature values ​​for each set of railway vehicle operation data in the set of output data.

[0113] (Aspect 8) determining possible causes of the set of abnormal stopping accuracy values ​​based on the set of feature values; including the candidate causes in the set of output data; The railway vehicle data analysis method according to any one of aspects 1 to 7, further comprising:

[0114] (Aspect 9) A railway vehicle data analysis device, comprising: a data acquisition unit configured to acquire a set of rail vehicle operating data related to operation of the rail vehicle; an analysis data extraction unit, identifying a first subset of rail vehicle operating data from the set of rail vehicle operating data using the set of stopping accuracy conditions, the first subset including a set of rail vehicle stopping accuracy values; determining, from the first subset of rail vehicle operating data, a second subset of rail vehicle operating data that includes an anomalous set of stopping accuracy values ​​using a predetermined stopping accuracy threshold; an analysis data extraction unit configured as follows: An analysis management unit, defining a set of analysis variables related to operational events that may affect the stopping accuracy of the rail vehicle; defining a set of analytic functions for identifying a set of analytic variables within the second subset of rail vehicle operating data; An analysis management unit configured as follows: an analysis unit, extracting, from the second subset of the rail vehicle operating data using the set of analytical functions, a third subset of the rail vehicle operating data corresponding to a time frame that includes the set of analytical variables; using statistical analysis techniques to identify a set of feature values ​​from the third subset of rail vehicle operating data that are indicative of potential causes of the set of anomalous stopping accuracy values; generating a set of output data indicative of a set of feature values ​​associated with the set of rail vehicle operating data; an analysis unit configured as follows: A railway vehicle data analysis device comprising:

[0115] (Aspect 10) 1. A rail vehicle data analysis computer program comprising a computer-readable storage medium having program instructions embodied thereon, the computer-readable storage medium not being a primary signal itself, the program instructions being executable by a processor, and causing the processor to: obtaining a set of rail vehicle operating data related to operation of the rail vehicle, the set of rail vehicle operating data including a speed value of the rail vehicle at a particular time while traveling along a route, a stopping accuracy value of the rail vehicle at a particular time while traveling along the route, and a next station tag indicating a subsequent station at which the rail vehicle is scheduled to stop on the route; identifying a first subset of rail vehicle operating data from the set of rail vehicle operating data using the set of stopping accuracy conditions, the first subset including a set of rail vehicle stopping accuracy values; identifying a first time-series driving record from the set of time-series driving records, wherein in the first time-series driving record: The speed value of the railcar is zero, The speed value of the railcar is greater than zero in the previous time-series operation record, In the immediately following time-critical driving record, the speed value of the railcar is zero, The next station tag is different from the next station tag of the immediately preceding time-series operation record. To identify and extracting, as a first subset of the railway vehicle operation data, a next station tag from a first time-series operation record and a stopping accuracy value from a second time-series operation record that is a predetermined number of records after the first time-series operation record; and determining, from the first subset of the rail vehicle operating data, a second subset of the rail vehicle operating data that includes an anomalous set of stopping accuracy values ​​using a predetermined stopping accuracy threshold; defining a set of analysis variables related to operational events that may affect stopping accuracy of the rail vehicle; defining a set of analytic functions to identify a set of analytic variables within the second subset of rail vehicle operating data; extracting, from the second subset of the rail vehicle operating data using the set of analytic functions, a third subset of the rail vehicle operating data corresponding to a time frame that includes the set of analytic variables; using statistical analysis techniques to identify a set of feature values ​​from the third subset of rail vehicle operating data that are indicative of potential causes of the set of anomalous stopping accuracy values; generating a set of output data indicative of a set of feature values ​​associated with the set of rail vehicle operating data; A computer program for analyzing railway vehicle data, comprising:

[0116] (Aspect 11) A railway vehicle data analysis system, comprising: Railway vehicles and A user terminal; a railway vehicle data analysis device; The railway vehicle data analysis device is equipped with a data acquisition unit configured to acquire a set of rail vehicle operating data related to operation of the rail vehicle; an analysis data extraction unit, identifying a first subset of rail vehicle operating data from the set of rail vehicle operating data using the set of stopping accuracy conditions, the first subset including a set of rail vehicle stopping accuracy values; determining, from the first subset of rail vehicle operating data, a second subset of rail vehicle operating data that includes an anomalous set of stopping accuracy values ​​using a predetermined stopping accuracy threshold; an analysis data extraction unit configured as follows: An analysis management unit, defining a set of analysis variables related to operational events that may affect the stopping accuracy of the rail vehicle; defining a set of analytic functions for identifying a set of analytic variables within the second subset of rail vehicle operating data; An analysis management unit configured as follows: an analysis unit, extracting, from the second subset of the rail vehicle operating data using the set of analytical functions, a third subset of the rail vehicle operating data corresponding to a time frame that includes the set of analytical variables; using statistical analysis techniques to identify a set of feature values ​​from the third subset of rail vehicle operating data that are indicative of potential causes of the set of anomalous stopping accuracy values; generating a set of output data indicative of the set of feature values ​​associated with the set of rail vehicle operating data, and outputting the set of output data to a user terminal; an analysis unit configured as follows: A railway vehicle data analysis system comprising:

[0117] The present invention may be a system, a method, and / or a computer program product, which may include a computer-readable storage medium having computer-readable program instructions for causing a processor to implement aspects of the present invention.

[0118] A computer-readable storage medium may be a tangible device capable of holding and storing instructions used by an instruction execution device. The computer-readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. A non-exhaustive list of more specific examples of computer-readable storage media includes portable computer diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), static random access memories (SRAMs), portable compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), memory sticks, floppy disks, mechanical encryption devices such as punch cards or groove ridge structures with instructions recorded on them, and any suitable combination of the above. As used herein, a computer-readable storage medium should not be construed as being itself a primary signal, such as an electric wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse passing through a fiber optic cable), or an electrical signal transmitted by an electrical wire.

[0119] Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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-readable program instructions.

[0120] The computer-readable program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to manufacture a machine, such that the instructions, executed by the processor of the computer or other programmable data processing apparatus, create means for performing the functions / acts specified in the flowchart and / or block diagram blocks. These computer-readable program instructions may also be stored on a computer-readable storage medium that can cause the computer, programmable data processing apparatus, and / or other device to function in a particular way, such that the computer-readable storage medium having the instructions stored thereon comprises an article of manufacture containing instructions that implement aspects of the functions / acts specified in the flowchart and / or block diagram blocks.

[0121] The computer-readable program instructions may further be loaded into a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be executed on the computer, other programmable apparatus, or other device to create a computer-implemented process, such that the instructions executing on the computer, other programmable apparatus, or other device perform the functions / acts specified in the flowchart and / or block diagram blocks.

[0122] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing a specialized logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It will also be appreciated that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by special-purpose hardware-based systems that perform specialized functions or operations or execute a combination of special-purpose hardware and computer instructions.

[0123] While the foregoing relates to exemplary embodiments of the present invention, other and further embodiments of the present invention may be devised without departing from the basic scope of the invention, which scope is determined by the following claims. The description of various embodiments of the present disclosure has been provided for illustrative purposes and is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein has been chosen to explain the principles of the embodiments, practical applications or technical improvements of existing technology, or to enable those skilled in the art to understand the embodiments disclosed herein.

[0124] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of various embodiments. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "set," "group," "bundle," and the like are intended to include one or more. Furthermore, it will be understood that the terms "comprise" and / or "comprising," as used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. In the foregoing detailed description of exemplary embodiments of various embodiments, reference has been made to the accompanying drawings, which form a part hereof, in which like numerals indicate like elements, and in which specific exemplary embodiments are shown, by way of example, and in which various embodiments may be practiced. Although the above embodiments have been described in sufficient detail to enable those skilled in the art to practice the embodiments, other embodiments may be used, and logical, mechanical, electrical, and other changes may be made without departing from the scope of the various embodiments. Numerous specific details have been set forth in the above description to provide a thorough understanding of the various embodiments. However, the various embodiments may be practiced without these specific details. In other instances, well-known circuits, structures, and techniques have not been shown in detail so as not to obscure the embodiments. [Explanation of symbols]

[0125] 200 Railway Vehicle Data Analysis System 210 Railroad Vehicles 220 User Terminal 230 Communication Network 240 Railway vehicle data analysis device 242 Data Acquisition Unit 244 Analysis Data Extraction Unit 246 Analysis and Management Unit 248 Analysis Unit

Claims

1. A railway vehicle data analysis method, comprising: obtaining a set of rail vehicle operating data relating to the operation of the rail vehicle; identifying a first subset of rail vehicle operating data from the set of rail vehicle operating data using a set of stopping accuracy conditions, the first subset including a set of stopping accuracy values ​​for the rail vehicle; determining, from the first subset of rail vehicle operating data, a second subset of rail vehicle operating data that includes an anomalous set of stopping accuracy values ​​using a predetermined stopping accuracy threshold; defining a set of analysis variables related to operational events that may affect stopping accuracy of the rail vehicle; defining a set of analysis functions to identify the set of analysis variables within the second subset of the rail vehicle operating data; extracting, from the second subset of the rail vehicle operating data using the set of analytic functions, a third subset of the rail vehicle operating data corresponding to a time frame that includes the set of analytic variables; using statistical analysis techniques to identify a set of feature values ​​from the third subset of the rail vehicle operating data that are indicative of potential causes of the set of stopping accuracy values ​​that are anomalous; and generating a set of output data indicative of the set of feature values ​​associated with the set of rail vehicle operating data; A railway vehicle data analysis method, including:

2. The set of railcar operation data includes a set of time-series operation records, and the set of time-series operation records includes: a speed value of the rail vehicle at a particular time while traveling along a route; a stopping accuracy value of the rail vehicle at a particular time while traveling along the route; and a next station tag indicating a subsequent station at which the rail vehicle is scheduled to stop on the route; The railway vehicle data analysis method according to claim 1 , comprising at least the steps of:

3. Identifying the first subset of the rail vehicle operating data includes: Identifying a first time-series operation record from the set of time-series operation records, wherein in the first time-series operation record: the speed value of the rail vehicle is zero; the speed value of the rail vehicle is greater than zero in a previous time-series driving record; In an immediately subsequent time-critical driving record, the speed value of the rail vehicle is zero; The next station tag is different from the next station tag of the immediately following time-series operation record, To identify and extracting, as the first subset of the railway vehicle operation data, the next station tag from the first time-series operation record and a stopping accuracy value from a second time-series operation record that is a predetermined number of records after the first time-series operation record; The railway vehicle data analysis method according to claim 2 , further comprising:

4. The railway vehicle data analysis method according to claim 3 , wherein the predetermined number of records after the first time-series operation record is based on a time-series record calculation time.

5. 2. The method of analyzing rail vehicle data of claim 1, wherein the set of analysis variables is selected from the group consisting of slip, speed, acceleration, overstop, and use of emergency interrupts.

6. Defining the set of analytic functions comprises: defining a composite analytic function including a first analytic function for identifying a first analytic variable of the set of analytic variables within the second subset of the rail vehicle operating data and a second analytic function for identifying a second analytic variable of the set of analytic variables within the second subset of the rail vehicle operating data. The railcar data analysis method according to claim 1 , further comprising:

7. The method of claim 1 , further comprising visually highlighting in the set of output data any feature values ​​in the set of feature values ​​that fail to achieve a normal operating range threshold.

8. determining possible causes of the set of abnormal stopping accuracy values ​​based on the set of feature values; including the potential causes in the set of output data; The railcar data analysis method according to claim 1 , further comprising:

9. A railway vehicle data analysis device, a data acquisition unit configured to acquire a set of rail vehicle operating data relating to the operation of the rail vehicle; an analysis data extraction unit, identifying a first subset of the rail vehicle operating data from the set of rail vehicle operating data using a set of stopping accuracy conditions, the first subset including a set of stopping accuracy values ​​for the rail vehicle; determining, from the first subset of rail vehicle operating data, a second subset of rail vehicle operating data that includes an anomalous set of stopping accuracy values ​​using a predetermined stopping accuracy threshold; an analysis data extraction unit configured as follows: An analysis management unit, defining a set of analysis variables related to operational events that may affect stopping accuracy of the rail vehicle; defining a set of analysis functions for identifying the set of analysis variables within the second subset of the rail vehicle operating data; An analysis management unit configured as follows: an analysis unit, extracting, from the second subset of the rail vehicle operating data using the set of analytic functions, a third subset of the rail vehicle operating data corresponding to a time frame that includes the set of analytic variables; using statistical analysis techniques to identify a set of feature values ​​from the third subset of the rail vehicle operating data that are indicative of potential causes of the set of anomalous stopping accuracy values; generating a set of output data indicative of the set of feature values ​​associated with the set of rail vehicle operating data; an analysis unit configured as follows: A railway vehicle data analysis device comprising:

10. 1. A rail vehicle data analysis computer program comprising a computer-readable storage medium having program instructions embodied thereon, the computer-readable storage medium not itself a transient signal, the program instructions being executable by a processor, the processor: obtaining a set of rail vehicle operating data related to operation of a rail vehicle, the set of rail vehicle operating data including a speed value of the rail vehicle at a particular time while traveling along a route, a stopping accuracy value of the rail vehicle at a particular time while traveling along the route, and a next station tag indicating a subsequent station at which the rail vehicle is scheduled to stop on the route; identifying a first subset of the rail vehicle operating data from the set of rail vehicle operating data using a set of stopping accuracy conditions, the first subset including a set of stopping accuracy values ​​for the rail vehicle, wherein identifying the first subset includes: Identifying a first time-series operation record from the set of time-series operation records, wherein in the first time-series operation record: the speed value of the rail vehicle is zero; the speed value of the rail vehicle is greater than zero in a previous time-series driving record; In an immediately subsequent time-critical driving record, the speed value of the rail vehicle is zero; The next station tag is different from the next station tag of the immediately following time-series operation record, To identify and extracting, as the first subset of the railway vehicle operation data, the next station tag from the first time-series operation record and a stopping accuracy value from a second time-series operation record that is a predetermined number of records after the first time-series operation record; and determining, from the first subset of the rail vehicle operating data, a second subset of the rail vehicle operating data that includes an anomalous set of stopping accuracy values ​​using a predetermined stopping accuracy threshold; defining a set of analysis variables related to operational events that may affect stopping accuracy of the rail vehicle; defining a set of analysis functions to identify the set of analysis variables within the second subset of the rail vehicle operating data; extracting, from the second subset of the rail vehicle operating data using the set of analytic functions, a third subset of the rail vehicle operating data corresponding to a time frame that includes the set of analytic variables; using statistical analysis techniques to identify a set of feature values ​​from the third subset of the rail vehicle operating data that are indicative of potential causes of the set of anomalous stopping accuracy values; and generating a set of output data indicative of the set of feature values ​​associated with the set of rail vehicle operating data; A computer program for analyzing railway vehicle data, comprising:

Images