Railway data management method, equipment and computer program product

Abstract

The invention relates to the technical field of railway traffic informatization, in particular to a railway data management method and device and a computer program product, and the method comprises the steps: obtaining the operation related data and part fault data of a railway vehicle; and generating maintenance management related data based on the operation related data and / or the part fault data of the railway vehicle. A data chain related to parts and transportation conditions is established by establishing an association relationship between a part maintenance process and conditions such as train marshalling, load and the like, and information such as marshalling, train order, position, empty weight, running direction, yellow mark, typical application fault, running mileage under various load tonnages, long ramp running conditions and the like of coupler accessories in the transportation process is integrated; a full-life-cycle data set including transportation and maintenance index parameters is formed through association of the unique identification ID and application fault tracking, fault maintenance and scrapping processes of the accessories, and support is provided for subsequent data management.

Description

Technical Field

[0001] The present disclosure relates to the technical field of railway traffic informatization, and particularly to a railway data management method, device and computer program product. Background Art

[0002] In recent years, the formation of railway trains has gradually advanced from 10,000-ton formations and 20,000-ton formations to "3+0" 10,000-ton formations. The increase in heavy-haul trains means that the traction force borne by the couplers and the braking force borne by the brake beams increase, and the degree of damage to the vehicle components responsible for functions such as traction and braking will continue to increase. It is of great significance to monitor the maintenance status of railway vehicles, scientifically evaluate them, accurately carry out repairs, and continuously improve.

[0003] However, currently, the basic information in the maintenance process of couplers and brake beams mainly includes the car numbers and positions of the unloaded cars, and is not associated with train formations, load weights, etc. At the same time, the data throughout the life cycle of components such as couplers and brake beams, which are important bases in the field of freight car maintenance, have not yet formed data assets. Summary of the Invention

[0004] The present disclosure provides a railway data management method, device and computer program product to solve the problems in the prior art that the maintenance process of railway vehicle components is not associated with train formations, load weights, etc., and the data throughout the life cycle of components has not been formed.

[0005] In a first aspect, the present disclosure provides a railway data management method, including: obtaining operation-related data and component failure data of railway vehicles; generating maintenance management-related data based on the operation-related data and / or component failure data of railway vehicles.

[0006] In some embodiments, generating maintenance management-related data based on the operation-related data and / or component failure data of railway vehicles includes: generating relevant data of components under normal circumstances or after maintenance based on the operation-related data of railway vehicles; generating relevant data of components in the case of component failures based on the operation-related data and component failure data of railway vehicles.

[0007] In some embodiments, the relevant data of components under normal circumstances or after maintenance includes at least one of the following: vehicle running mileage data on long and steep slopes; vehicle formation position running mileage data.

[0008] In some embodiments, the relevant data of components in the case of component failures includes at least one of the following: component failure running mileage data on long and steep slopes; component failure position running mileage data.

[0009] In some embodiments, the operation-related data of railway vehicles includes at least one of the following: data related to passing train messages; data related to vehicle information; data related to detection stations.

[0010] In some embodiments, the vehicle's long and steep slope operation mileage data includes at least one of the following: the total operation mileage data corresponding to the long and steep slope section, the empty vehicle mileage data, the loaded vehicle mileage data, the ten thousand - ton empty vehicle mileage data, the ten thousand - ton loaded vehicle mileage data, the two - ten - thousand - ton empty vehicle mileage data, the two - ten - thousand - ton loaded vehicle mileage data, the ten thousand - ton mileage data after maintenance, and the two - ten - thousand - ton mileage data after maintenance.

[0011] In some embodiments, the vehicle formation position operation mileage data includes at least one of the following: the total operation mileage data corresponding to each vehicle formation, the empty vehicle mileage data, the loaded vehicle mileage data, the ten thousand - ton empty vehicle mileage data, the ten thousand - ton loaded vehicle mileage data, the two - ten - thousand - ton empty vehicle mileage data, the two - ten - thousand - ton loaded vehicle mileage data, the ten thousand - ton mileage data after maintenance, and the two - ten - thousand - ton mileage data after maintenance.

[0012] In some embodiments, the method further includes: managing the entire life cycle of the components based at least on the maintenance management - related data.

[0013] In a second aspect, the present disclosure provides a computer device, including a memory, a processor, and a computer program stored on the memory, where the processor executes the computer program to implement the steps of the method described in the above aspect.

[0014] In a third aspect, the present disclosure provides a computer program product, including a computer program / instructions, where when the computer program is executed by a processor, it implements the steps of the method described in the above aspect.

[0015] A railway data management method, device, and computer program provided by the present disclosure establish an association relationship between the component maintenance process and the train formation, load, etc.; thereby establishing a data chain related to components and transportation conditions, collecting information such as the formation, vehicle sequence, position, empty / loaded weight, running direction, yellow label, typical operation failures, operation mileage under each load tonnage, and long and steep slope operation conditions of coupler fittings during transportation, and associating through a unique identifier ID with the operation failure tracking, maintenance failure, and scrapping process of the fittings, forming a data set for the entire life cycle including transportation and maintenance index parameters. This provides strong data support for subsequent establishment of failure law models for fittings such as coupler buffers and brake beams. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Hereinafter, the present disclosure will be described in more detail based on embodiments with reference to the drawings:

[0017] Figure 1 It is a method flow chart of a railway data management method provided by an embodiment of the present disclosure.

[0018] Figure 2 It is a schematic diagram of the railway data flow process provided by an embodiment of the present disclosure.

[0019] Figure 3 Code illustration for calculating the running mileage of a vehicle on a long and steep slope provided by an embodiment of the present disclosure Figure 1 。

[0020] Figure 4 Code illustration for calculating the running mileage of a vehicle on a long and steep slope provided by an embodiment of the present disclosure Figure 2 。

[0021] Figure 5 Code illustration for calculating the running mileage of a vehicle on a long and steep slope provided by an embodiment of the present disclosure Figure 3 。

[0022] Figure 6 Code illustration for calculating the running mileage of a vehicle on a long and steep slope provided by an embodiment of the present disclosure Figure 4 。

[0023] Figure 7 Code illustration for calculating the running mileage data of the vehicle formation position provided by an embodiment of the present disclosure Figure 1 。

[0024] Figure 8 Code illustration for calculating the running mileage data of the vehicle formation position provided by an embodiment of the present disclosure Figure 2 。

[0025] Figure 9 Code illustration for calculating the running mileage data of the vehicle formation position provided by an embodiment of the present disclosure Figure 3 。

[0026] Figure 10 Code illustration for calculating the running mileage data of the long and steep slope with component failures provided by an embodiment of the present disclosure Figure 1 。

[0027] Figure 11 Code illustration for calculating the running mileage data of the long and steep slope with component failures provided by an embodiment of the present disclosure Figure 2 。

[0028] Figure 12 Code illustration for calculating the running mileage data of the long and steep slope with component failures provided by an embodiment of the present disclosure Figure 3 。

[0029] Figure 13 Code illustration for calculating the running mileage data of the component failure position provided by an embodiment of the present disclosure Figure 1 。

[0030] Figure 14 Code illustration for calculating the running mileage data of the component failure position provided by an embodiment of the present disclosure Figure 2 。

[0031] Figure 15Code schematic for calculating the operating mileage data of component failures provided by the embodiments of the present disclosure Figure 3 。

[0032] Figure 16 Schematic diagram of a railway data management device provided by the embodiments of the present disclosure.

[0033] In the drawings, the same components are denoted by the same reference numerals, and the drawings are not drawn to actual scale. Detailed implementation manners

[0034] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, and to fully understand how the present disclosure uses technical means to solve technical problems and the implementation process of achieving corresponding technical effects and implement accordingly, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The embodiments of the present disclosure and each feature in the embodiments can be combined with each other without conflict, and the formed technical solutions are all within the protection scope of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present disclosure.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0036] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0037] Example 1

[0038] With the development of the economy and the pursuit of transportation efficiency, transportation organization technology has been continuously evolving. One notable manifestation is the increase in heavy-haul trains. By optimizing train formation, using more powerful locomotive traction, improving transportation scheduling and other technical and management means, a single train can transport more goods, achieve economies of scale, improve transportation efficiency and reduce transportation costs. Currently, train formation has gradually advanced from 10,000-ton formation and 20,000-ton formation to "3+0" 10,000-ton formation.

[0039] However, the changes in transportation organization technology pose new challenges to the operation safety of vehicles. The increase in heavy-haul trains means that the coupler bears more traction force and the brake beam bears more braking force, and the degree of damage to the vehicle components that undertake functions such as traction and braking will continue to increase. Therefore, it is of great significance to monitor the maintenance status of railway vehicles, scientifically evaluate them, precisely carry out repairs, and continuously improve.

[0040] Currently, the basic information in the maintenance process of couplers and brake beams is mainly the car numbers and positions of the unloaded vehicles, which is not associated with the formation and heavy-haul conditions of the couplers at various time periods during transportation. It is difficult to conduct effective correlation analysis between transportation formation changes and maintenance failures, and it is even more impossible to synchronously achieve improvements in maintenance processes, production, and quality according to the changes in transportation formation technology to better ensure transportation safety.

[0041] At the same time, the correlation between the service life of components such as couplers and brake beams and the axle load, formation length, and operating mileage of vehicles will be an important basis for formulating relatively scientific and reasonable maintenance cycles and service lives. The data throughout the life cycle of components such as couplers and brake beams will be important data assets in the field of freight car maintenance, but they have not been formed yet.

[0042] To solve the above problems, the present disclosure proposes a railway data management method, device, and computer program product. Specifically as Figures 1 - 15 described.

[0043] Figure 1 is a schematic flow chart of a railway data management method provided by an embodiment of the present disclosure. As Figure 1 shown, a railway data management method may at least include the following steps:

[0044] In step S101, obtain the operation-related data of railway vehicles and component failure data. As described above, both heavy-haul and formation are important factors affecting the damage of railway vehicle components. Therefore, the present disclosure proposes to associate the maintenance process of railway vehicle components with the formation, load, etc. of trains to provide data support for subsequent management. This step is to obtain data.

[0045] In step S102, maintenance management related data is generated based on the operation related data of railway vehicles and / or component failure data. As described above, based on the data obtained in step S101, maintenance management related data in different situations is generated for subsequent data or maintenance management.

[0046] Example 2

[0047] Based on the above embodiments, step S102, generating maintenance management related data based on the operation related data of railway vehicles and / or component failure data may further include:

[0048] In step S1021, related data under normal conditions or after maintenance of components is generated based on the operation related data of railway vehicles.

[0049] In step S1022, related data in the case of component failure is generated based on the operation related data of railway vehicles and component failure data.

[0050] That is to say, the maintenance management related data in this state can be calculated respectively according to the state of the components.

[0051] Example 3

[0052] Based on the above embodiments, the related data under normal conditions or after maintenance may include at least one of the following: the running mileage data of the vehicle on long and steep slopes; the running mileage data of the vehicle in the formation position.

[0053] Among them, as a kind of terrain, the long and steep slope section has a great impact on components. On the long and steep slope section, railway vehicles need to overcome the change of gravitational potential energy, and the traction force borne by the coupler and the braking force borne by the brake beam will increase significantly. For example, when a heavy-haul train is running on a long and steep slope, the coupler may bear several times the tension when running on flat roads, which will accelerate the fatigue and wear of the coupler, and may even lead to serious failures such as coupler fracture. During frequent and high-intensity braking, the temperature of the brake beam will rise sharply, and problems such as thermal fatigue and excessive wear of the brake shoe are likely to occur, affecting the braking performance and safety. Therefore, the long and steep slope mileage data of the vehicle is of great significance for the safe operation of the vehicle, component maintenance, and operation cost control management, etc.

[0054] Marshalling refers to the process of combining railway vehicles together according to certain rules and requirements to form a complete train. For freight trains, different types of freight cars are combined according to the types of goods transported and transportation requirements. For example: gondola cars (suitable for transporting bulk goods such as coal and ore), boxcars (suitable for transporting goods afraid of moisture and sunlight, such as grain and precision instruments), flatcars (suitable for transporting large mechanical equipment, steel, etc.), tank cars (suitable for transporting liquid or gaseous goods, such as petroleum and chemical raw materials). The marshalling position will have varying degrees of impact on the parts of railway freight cars through various factors such as force, braking, and the environment. During the design, manufacturing, maintenance, and operation of railway freight cars, an appropriate marshalling position can be selected to improve the reliability and durability of the parts and ensure the safe operation of railway freight cars.

[0055] Example 4

[0056] On the basis of the above embodiments, exemplarily, the vehicle's long and steep slope operation mileage data may further include at least one of the following: the total operation mileage data corresponding to the long and steep slope section, the empty car mileage data, the loaded car mileage data, the ten thousand - ton empty car mileage data, the ten thousand - ton loaded car mileage data, the two - ten - thousand - ton empty car mileage data, the two - ten - thousand - ton loaded car mileage data, the ten thousand - ton mileage data after maintenance, and the two - ten - thousand - ton mileage data after maintenance.

[0057] As mentioned above, the load is closely related to the damage of parts, so the vehicle's long and steep slope operation mileage data can be further segmented to understand the specific data under different loads. It can be understood that with the future management requirements or the development of railway trains, the segmentation dimensions can be further increased or modified, and the present disclosure does not make specific limitations.

[0058] Example 5

[0059] On the basis of the above embodiments, exemplarily, the vehicle's marshalling position operation mileage data may further include at least one of the following: the total operation mileage data corresponding to each vehicle marshalling, the empty car mileage data, the loaded car mileage data, the ten thousand - ton empty car mileage data, the ten thousand - ton loaded car mileage data, the two - ten - thousand - ton empty car mileage data, the two - ten - thousand - ton loaded car mileage data, the ten thousand - ton mileage data after maintenance, and the two - ten - thousand - ton mileage data after maintenance.

[0060] Similarly, the vehicle's marshalling position operation mileage data under different marshalling positions is segmented, and the segmentation dimensions are not limited either.

[0061] Example 6

[0062] On the basis of the above embodiments, the relevant data in the case of component failures may include at least one of the following: the long and steep slope operation mileage data of component failures; the operation mileage data of component failure positions.

[0063] During transportation, when a component fails, the train usually does not stop immediately to repair the faulty component, but continues to drive, so relevant data on the component failure will be generated.

[0064] Example 7

[0065] On the basis of the above-mentioned embodiment, the operation-related data of the railway vehicle may include at least one of the following: data related to vehicle passing messages; data related to vehicle information; and data related to inspection sites.

[0066] Example 8

[0067] On the basis of the above-mentioned embodiments, the whole life cycle of components is managed at least based on the maintenance management related data.

[0068] Example 9

[0069] On the basis of the above embodiments, this embodiment provides an application example. With the help of big data technology, this example accesses, processes, and stores vehicle passing message data, vehicle information data, mileage data in the direction of inspection stations, and component failure data, builds an association rule model, and designs an offline data warehouse based on dimensional modeling theory. In the offline data warehouse, data can be hierarchically transferred, filtered, associated, and calculated to support the realization of indicators such as grouping, vehicle sequence, position, empty weight, running direction, changes in typical failures, vehicle mileage under various load tonnages, and mileage on long and steep slopes. For details, please refer to Figures 2 - 15 Describe in detail.

[0070] Figure 2 The schematic diagram of the railway data transfer process provided by the embodiment of the present disclosure is as follows. Figure 2 As shown, the data flow process in the application example of the present disclosure can be as follows.

[0071] 1. Data acquisition: Obtain railway vehicle operation-related data and component failure data from the database.

[0072] Specifically, the required data can be obtained from a database (such as an Oracle database and an Hbase database) storing railway vehicle operation-related data and / or component failure data. The railway vehicle operation-related data may include at least one of the following: vehicle passing message-related data; vehicle information-related data; and detection station-related data.

[0073] In this example, the data related to the vehicle passing message may include: vehicle passing message information data; the vehicle information related data may include: the vehicle's latest location information data; the detection site related data may include: site direction mileage information data.

[0074] After obtaining the data, on the big data cluster, map the original data into a tabular form and then migrate it to the ODS source layer.

[0075] 2. Data preprocessing: Preprocess the data obtained in the previous step to ensure the availability and accuracy of the data.

[0076] Specifically, perform data cleaning, data transformation and other preprocessing on problems such as data inconsistency, non-standardization, duplicate redundancy, etc. in the ODS source layer.

[0077] For example, for the problem of inconsistent formats of passing vehicle time data in vehicle passing message information. According to the importance and business attributes of the data, use conditional functions to perform conditional judgments on the data formats: Do not process the data that meets the format requirements, and perform format conversion on the data that does not meet the format requirements.

[0078] Another example is that when a vehicle passes through a detection station, the detection equipment may perform repeated detections, resulting in duplicate redundancy of the passing vehicle message information data. To avoid subsequent repeated calculations of the data, a processing rule can be set to retain only the first record within 6 hours for the data collected by the same vehicle on the same equipment.

[0079] At the same time, the vehicle message information data does not include data such as the detection equipment ID, vehicle position, and vehicle running direction, but these data are necessary information and can be used as associated data or calculation basis in subsequent calculations. Therefore, the missing necessary information data will also be filtered in the preprocessing step to ensure the integrity and accuracy of the processed data.

[0080] Then, store the preprocessed data in the DWD detail layer.

[0081] 3. Data calculation and processing: Generate maintenance management-related data based on the operation-related data of railway vehicles and / or component failure data.

[0082] Specifically, generate maintenance management-related data in different dimensions based on the operation-related data of railway vehicles and / or component failure data that have been preprocessed in the previous step.

[0083] Furthermore, relevant data under normal conditions or after maintenance of components can be generated based on the operation-related data of railway vehicles; relevant data under component failure conditions can also be generated based on the operation-related data of railway vehicles and component failure data.

[0084] Among them, the relevant data under normal circumstances or after maintenance of the components may include at least one of the following: the vehicle's long uphill running mileage data, the vehicle's formation position running mileage data; the relevant data in case of component failure may include at least one of the following: the component failure long uphill running mileage data, the component failure position running mileage data. The following will be described one by one.

[0085] 1) Vehicle's long uphill running mileage data

[0086] The vehicle's long uphill running mileage data refers to the running mileage of the vehicle on the long uphill section, which may include: the total running mileage corresponding to the long uphill section, the empty car mileage, the loaded car mileage, the ten thousand ton empty car mileage, the ten thousand ton loaded car mileage, the two ten thousand ton empty car mileage, the two ten thousand ton loaded car mileage, the ten thousand ton mileage after maintenance, the two ten thousand ton mileage after maintenance, and other data.

[0087] Specifically, it can be calculated based on the running related data of railway vehicles (such as: the passing vehicle information message data of the long uphill section, the vehicle's last position information data, the length of the long uphill section, etc.). The specific calculation method can be as Figures 3 - 6 shown.

[0088] Figure 3 Code illustration for calculating the vehicle's long uphill running mileage provided by an embodiment of the present disclosure Figure 1 . As Figure 3 shown, first, the passing vehicle information message data and the vehicle's last position information data are associated through the vehicle number to complete the vehicle related information. At the same time, each piece of data is marked according to the detection device number (the data belonging to the device numbers with the same empty / loaded situation, running direction, and the long uphill section to which they belong are marked with the same mark). The data obtained in this way is used as sub-table 1.

[0089] Figure 4 Code illustration for calculating the vehicle's long uphill running mileage provided by an embodiment of the present disclosure Figure 2 . As Figure 4 shown, then, for sub-table 1, the count() function can be used to count the number of times each vehicle passes through each detection device. For the devices on the same long uphill section, only one record with the most passing times needs to be selected. Multiplying this maximum number of times by the length of the long uphill section can obtain the total mileage of the vehicle on this long uphill section. Aggregating the total mileage of all long uphill sections with the sum() function can obtain the total long uphill running mileage of the vehicle. At the same time, the if() function can be used to judge according to the previously marked empty / loaded mark and the number of vehicles in the train formation to obtain the empty car long uphill mileage, the loaded car long uphill mileage, the ten thousand ton empty car long uphill mileage, the ten thousand ton loaded car long uphill mileage, the two ten thousand ton empty car long uphill mileage, and the two ten thousand ton loaded car long uphill mileage data. The data obtained in this way forms sub-table 2.

[0090] Figure 5 Code illustration for calculating the long and steep slope operation mileage of vehicles provided by the embodiments of the present disclosure Figure 3 As Figure 5 shown, in order to calculate the operation mileage after maintenance, we need to perform a self-join operation on the above-mentioned sub-table 1 to obtain all the data after the maintenance time. Then, calculate the long and steep slope mileage after 10,000-ton maintenance and the long and steep slope mileage after 20,000-ton maintenance according to the method in sub-table 2. The data obtained in this way forms sub-table 3.

[0091] Figure 6 Code illustration for calculating the long and steep slope operation mileage of vehicles provided by the embodiments of the present disclosure Figure 4 As Figure 6 shown, finally, associate and integrate sub-table 2 and sub-table 3 through the vehicle number to obtain the data table of the long and steep slope operation mileage of the vehicle, and store it in the DWS summary layer.

[0092] 2) Operation mileage data of vehicle formation positions

[0093] The operation mileage data of vehicle formation positions refers to the operation mileage of vehicles in different positions of the train formation, which may include: the total operation mileage, empty car mileage, loaded car mileage, 10,000-ton empty car mileage, 10,000-ton loaded car mileage, 20,000-ton empty car mileage, 20,000-ton loaded car mileage, mileage after 10,000-ton maintenance, mileage after 20,000-ton maintenance, etc. of each vehicle formation.

[0094] Specifically, it can be calculated based on the operation-related data of railway vehicles (such as: passing vehicle information message data, vehicle last position information data, station direction mileage information data, etc.). The specific calculation method can be as Figures 7 - 9 shown.

[0095] Figure 7 Code illustration for calculating the operation mileage data of vehicle formation positions provided by the embodiments of the present disclosure Figure 1 First, associate the passing vehicle information message data with the vehicle last position information data through the vehicle number to complete the vehicle-related information. At the same time, use the window function to sort the same vehicle according to the passing time. The data obtained in this way is used as sub-table (1).

[0096] Figure 8 Code illustration for calculating the operation mileage data of vehicle formation positions provided by the embodiments of the present disclosure Figure 2 Then, perform a self-join operation on sub-table (1), and associate the two adjacent pieces of data sorted according to the passing time. Use the concat() function to splice the two pieces of data together in the same format as the detection equipment number, vehicle operation direction of the first piece of data and the same information of the second piece of data to form a marked field. In this way, the information of a single station can be converted into the route information of the vehicle passing through adjacent stations. The optimized data obtained is used as sub-table (2).

[0097] Figure 9 Code schematic for calculating the operating mileage data of vehicle formation positions provided by embodiments of the present disclosure Figure 3 Finally, the sub-table (2) and the station-direction mileage information data table are associated through the marker field of the sub-table (2) and the ref_id field in the station-direction mileage information data table, so that the mileage information and vehicle empty / heavy weight information of each section of the line can be obtained. Then, according to the calculation method of the long and steep slope mileage, the total mileage, empty car mileage, heavy car mileage, 10,000-ton empty car mileage, 10,000-ton heavy car mileage, 20,000-ton empty car mileage, 20,000-ton heavy car mileage, 10,000-ton mileage after overhaul, and 20,000-ton mileage after overhaul of different vehicle formation positions can be aggregated and calculated for each vehicle, and the final vehicle formation position mileage data table is obtained and stored in the DWS summary layer.

[0098] 3) Operating mileage data of component failures on long and steep slopes

[0099] During operation, component failures may occur in the vehicle. At this time, the train will not stop immediately to deal with the faulty components, but will continue to run until it reaches the maintenance station. Since the specific location where the component failure occurs cannot be determined, it is approximately considered that the first detection station passed after the component failure is the location where the component failure occurs. Based on this basis, the operating mileage on the long and steep slope section during the component failure is calculated.

[0100] Specifically, it can be calculated based on the operation-related data of railway vehicles (such as: passing vehicle information message data of long and steep slope sections, long and steep slope section length data, etc.) and component failure data. The specific calculation method can be as Figures 10 - 12 shown.

[0101] Figure 10 Code schematic for calculating the operating mileage data of component failures on long and steep slopes provided by embodiments of the present disclosure Figure 1 First, the component failure data is associated with the passing vehicle information message data through the vehicle number, relevant information is supplemented, and the passing vehicle message data after the component failure and before the failure cancellation is screened out. At the same time, each piece of data is marked according to the detection equipment number (data with the same equipment number with the same empty / heavy weight situation, running direction, and belonging long and steep slope section is marked with the same mark). The data processed in this way will be used as sub-table <1>.

[0102] Figure 11 Code schematic for calculating the operating mileage data of component failures on long and steep slopes provided by embodiments of the present disclosure Figure 2。Then, for sub-table <1>, the count() function can be used to calculate the number of times each vehicle passes through each detection device. For the devices on the same long and steep slope section, only select the record with the most passing times. Multiply the maximum number of times by the length of this long and steep slope section to obtain the total mileage of the vehicle running on this long and steep slope section. Aggregate the total mileage of all long and steep slope sections through the sum() function to obtain the total long and steep slope running mileage of the vehicle. The data obtained in this way will be used as sub-table <2>.

[0103] Figure 12 Code illustration for calculating the long and steep slope running mileage data of component failures provided by this embodiment of the disclosure Figure 3 。Finally, use the window function on sub-table <1>, sort by passing time according to the same vehicle, the same faulty component, and the same fault cancellation time, to obtain the data of the first detection site passed after the fault occurs. Then calculate the extra driving mileage according to the detection device number, and associate this data with sub-table <2>. Subtract the extra driving mileage from the total mileage in sub-table <2> to obtain the actual running mileage of the component failure on the long and steep slope. Finally, store this component failure long and steep slope running mileage data table in the DWS summary layer.

[0104] 4) Running mileage data of component failure ranking

[0105] The running mileage data of component failure ranking refers to the running mileage of a vehicle at different formation rankings during the stage of continuing to drive after a component failure occurs during operation. The specific calculation method can be as Figures 13 - 15 shown.

[0106] Figure 13 Code illustration for calculating the running mileage data of component failure ranking provided by this embodiment of the disclosure Figure 1 。First, associate the component failure data with the passing vehicle information message data through the vehicle number to complete relevant information, and filter out the passing vehicle information message data after the component failure occurs but before the fault is cancelled. At the same time, use the window function to sort the same vehicle according to the passing time, and the data obtained after sorting is used as sub-table ①.

[0107] Figure 14 Code illustration for calculating the running mileage data of component failure ranking provided by this embodiment of the disclosure Figure 2Next, perform a self-join operation on Sub-table ①, and associate the adjacent two pieces of data sorted by passing vehicle time. Then, use the CONCAT() function to splice these two pieces of data together according to the detection device number and vehicle running direction of the first piece of data, as well as the same information and format of the second piece of data, as the marked field. In this way, the information of a single station can be converted into the route information of the vehicle passing through adjacent stations. The generated data will be used as Sub-table ②.

[0108] Figure 15 Code schematic for calculating the operating mileage data of component failure positions provided by the embodiments of the present disclosure Figure 3 Finally, associate Sub-table ② with the station direction mileage information data table through the marked field of Sub-table ② and the ref_id field in the station direction mileage information data table. In this way, the mileage information and vehicle empty and heavy information of each section of the line can be obtained. Then, group and aggregate according to vehicle number, failure number, component number, failure discovery time, failure cancellation time, and vehicle position to obtain the operating mileage of component failure positions. Finally, store the result in the component failure position operating mileage data table in the DWS summary layer.

[0109] In summary, finally in the DWS summary layer, a vehicle long gradient operating mileage data table, a vehicle formation position operating mileage data table, a component failure long gradient operating mileage data table, and a component failure position operating mileage data table can be obtained.

[0110] Subsequently, these tables in the DWS summary layer can be stored in the ORACLE database and connected to the data governance platform for subsequent management. The specific content of the management can include but is not limited to maintenance management, data management, etc., and the present disclosure does not make any restrictions.

[0111] At the same time, the correlation between the service life of components such as couplers and draft gears, and brake beams and the vehicle axle load, formation length, and operating mileage can be used as an important basis for formulating scientific and reasonable maintenance cycles and service lives. Therefore, the data of the entire life cycle of components such as couplers and draft gears, and brake beams can be used as important data assets in the field of freight car maintenance. Specifically:

[0112] It is possible to establish a data chain for maintenance and operation processes based on the unique ID identification of accessories such as coupler and draft gear devices, and brake beams, and form special data assets by analyzing the impact of formation mode changes on accessory failures.

[0113] ① Establish a unique ID for the accessory. Distinguish new and old products of couplers and draft gears and brake beams for non-condition-based maintenance vehicles. When newly purchased accessories are transported to the factory or depot maintenance sites and are not received in the vehicle, the system generates a unique ID for them. The ID of the old couplers and draft gears and brake beams is automatically generated by the system and shared in the loading and unloading process and the HCCBM system.

[0114] ② Establish a basic information database with the unique ID of the coupler draft gear and brake beam throughout the whole process as the retrieval key, including assembly information, dimension information, current vehicle loading information, mileage information, report information, status information, trajectory information, and formation information.

[0115] ③ Establish a database of coupler draft gear and brake beam overhaul, operation failures, and multi-T failures with the unique ID of the coupler draft gear and brake beam as the retrieval key, and associate the failures of the coupler draft gear and brake beam with the operating mileage.

[0116] ④ Establish organizations such as bearing long ramp operating mileage tracking, bearing formation position operating mileage update, and bearing railway line operating mileage tracking with the unique ID of the coupler draft gear and brake beam as the retrieval key.

[0117] ⑤ Expand the multi-T failures from using the car number as the retrieval condition to using the ID of the coupler draft gear and brake beam parts as the retrieval condition; optimize the algorithm for tracking the operating mileage of the coupler draft gear and brake beam parts, and conduct targeted optimization for incorrect multi-T passing vehicle data.

[0118] Example 10

[0119] Based on the above embodiments, this embodiment provides a railway data management device, which can be specifically as Figure 16 shown.

[0120] Figure 16 is a schematic diagram of a railway data management device provided by an embodiment of the present disclosure. Among them, the railway data management device 1600 can at least include the following modules:

[0121] An acquisition module 1601, configured to acquire operation-related data of railway vehicles and component failure data. Among them, the operation-related data of railway vehicles includes at least one of the following: passing vehicle message data; vehicle information data; detection site direction mileage data.

[0122] A generation module 1602, configured to generate overhaul management-related data based on the operation-related data of railway vehicles and / or component failure data.

[0123] Specifically, the generation module 1602 can further include:

[0124] A first generation unit 16021, configured to generate relevant data of components under normal conditions or after overhaul based on the operation-related data of railway vehicles.

[0125] Among them, the relevant data of components under normal conditions or after overhaul includes at least one of the following: vehicle long ramp operating mileage data; vehicle formation position operating mileage data.

[0126] Among them, the vehicle's long and steep slope operation mileage data includes at least one of the following: the total operation mileage data corresponding to the long and steep slope section, the empty vehicle mileage data, the loaded vehicle mileage data, the ten thousand - ton empty vehicle mileage data, the ten thousand - ton loaded vehicle mileage data, the two - ten - thousand - ton empty vehicle mileage data, the two - ten - thousand - ton loaded vehicle mileage data, the ten thousand - ton mileage data after overhaul, and the two - ten - thousand - ton mileage data after overhaul.

[0127] The vehicle formation position operation mileage data includes at least one of the following: the total operation mileage data corresponding to each vehicle formation, the empty vehicle mileage data, the loaded vehicle mileage data, the ten thousand - ton empty vehicle mileage data, the ten thousand - ton loaded vehicle mileage data, the two - ten - thousand - ton empty vehicle mileage data, the two - ten - thousand - ton loaded vehicle mileage data, the ten thousand - ton mileage data after overhaul, and the two - ten - thousand - ton mileage data after overhaul.

[0128] The second generation unit 16022 is used to generate relevant data in the case of component failure based on the operation - related data and component failure data of railway vehicles.

[0129] Among them, the relevant data in the case of component failure includes at least one of the following: the long and steep slope operation mileage data of component failure; the position operation mileage data of component failure.

[0130] In addition, the railway data management device 1600 may further include:

[0131] The management module 1603 manages the entire life cycle of the components at least based on the overhaul management - related data.

[0132] Example 11

[0133] Based on the above - mentioned embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the method described in the above - mentioned embodiments.

[0134] In some implementation manners of this embodiment, a computer - readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in the above - mentioned embodiments are implemented.

[0135] In some implementation manners of this embodiment, a computer program product is provided, including a computer program / instructions, and when the computer program is executed by a processor, the steps of the method described in the above - mentioned embodiments are implemented.

[0136] The processor may include, but is not limited to, for example, one or more processors or microprocessors, etc. Each processor may be implemented by an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is used to execute the methods in the above embodiments.

[0137] The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof. The computer-readable storage medium may include, but is not limited to, for example, a Random Access Memory (RAM), a Read-Only Memory (ROM), a flash memory, an EPROM memory, an EEPROM memory, a register, a computer storage medium (such as a hard disk, a floppy disk, a solid-state drive, a removable disk, a CD-ROM, a DVD-ROM, a Blu-ray disc, etc.).

[0138] The computer-readable storage medium may also store at least one computer-executable program / instructions, and the computer-executable program / instructions are, for example, computer-readable instructions. The computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, a Random Access Memory (RAM) and / or a cache, etc. The computer-readable storage medium may include, for example, a Read-Only Memory (ROM), a hard disk, a flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer. Then, when the computing device runs the computer-readable instructions stored on the computer-readable storage medium, the various methods described above may be performed.

[0139] In addition, the computer device may also include (but is not limited to) a data bus, an Input / Output (I / O) bus, a display, and input / output devices (such as a keyboard, a mouse, a speaker, etc.).

[0140] The processor may communicate with external devices via the I / O bus through a wired or wireless network.

[0141] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein when one or more computer-executable instructions are run by a processor, each function and / or step of the method in the embodiments described in the present technology is performed.

[0142] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods may also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0143] It should be noted that in the present disclosure, the terms "include", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article, or device. Without further limitation, an element limited by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article, or device including the element.

[0144] Although the disclosed embodiments of the present disclosure are as above, the above content is only an embodiment adopted for the convenience of understanding the present disclosure and is not intended to limit the present disclosure. Any person skilled in the art within the technical field to which the present disclosure pertains may make any modifications and changes in the form of implementation and details without departing from the spirit and scope disclosed by the present disclosure. However, the scope of patent protection of the present disclosure shall still be subject to the scope defined by the appended claims.

Claims

1. A railway data management method, characterized in that, Including: Obtaining operation-related data of railway vehicles and component failure data; Generating maintenance management-related data based on the operation-related data of the railway vehicles and / or the component failure data.

2. The method according to claim 1, wherein The generating of the maintenance management-related data based on the operation-related data of the railway vehicles and / or the component failure data includes: Generating relevant data of the components under normal conditions or after maintenance based on the operation-related data of the railway vehicles; Generating relevant data of the components in case of component failure based on the operation-related data of the railway vehicles and the component failure data.

3. The method according to claim 2, wherein The relevant data of the components under normal conditions or after maintenance includes at least one of the following: Operation mileage data of the vehicle on long and steep slopes; Operation mileage data of the vehicle in the formation position.

4. The method according to claim 2, wherein The relevant data of the components in case of component failure includes at least one of the following: Operation mileage data of the component failure on long and steep slopes; Operation mileage data of the component failure in the formation position.

5. The method according to any one of claims 1 to 4, characterized in that, The operation-related data of the railway vehicles includes at least one of the following: Relevant data of passing vehicle messages; Relevant data of vehicle information; Relevant data of detection sites.

6. The method according to claim 3, characterized in that, The operation mileage data of the vehicle on long and steep slopes includes at least one of the following: Total operation mileage data corresponding to the long and steep slope section, empty car mileage data, loaded car mileage data, empty car mileage data per 10,000 tons, loaded car mileage data per 10,000 tons, empty car mileage data per 20,000 tons, loaded car mileage data per 20,000 tons, mileage data after maintenance per 10,000 tons, mileage data after maintenance per 20,000 tons.

7. The method according to claim 3, wherein The operation mileage data of the vehicle in the formation position includes at least one of the following: Total operation mileage data corresponding to each vehicle formation, empty car mileage data, loaded car mileage data, empty car mileage data per 10,000 tons, loaded car mileage data per 10,000 tons, empty car mileage data per 20,000 tons, loaded car mileage data per 20,000 tons, mileage data after maintenance per 10,000 tons, mileage data after maintenance per 20,000 tons.

8. The method according to claim 1, wherein The method further includes: Managing the entire life cycle of the components based at least on the maintenance management-related data.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 8.

10. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 8.

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