Train data recording system with whole-network recording capability

Abstract

The invention provides a train data recording system with a whole-network recording capability, and the system comprises a bottom layer function module which is used for achieving the full collection and periodic recording of train network data, and providing a compressed binary stream data file for a middle layer module; the middle layer module is used for carrying out decompression, format conversion and time-sharing data reduction on the compressed binary stream file and generating a structured CSV data file for the data analysis adaptation module; and the data analysis adaptation module is used for performing analysis adaptation on the structured CSV data file, extracting key information and generating a standardized data interface for train operation analysis. By adopting a compression mechanism, flexible port configuration and a time-sharing data dynamic reduction technology, the problems of performance bottleneck, data loss and high operation and maintenance cost of the traditional ERM in a large-scale data recording process are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of train data, in particular, especially relates to a train data recording system with full-network recording capability. BACKGROUND

[0002] The train data recording system plays a key role in ensuring the safe operation of the train. During the operation of the train, the system continuously records various important information such as train control commands, device working states, and fault conditions, etc., to provide original data support for subsequent analysis of train operation conditions, fault diagnosis, and optimization of train performance. The train data recording system mainly records two types of data: periodic data and jump trigger data. Periodic data is recorded by ERM every fixed time, which occupies most of the space of ERM recording. Jump trigger data is recorded when the data jumps, which is mainly used for fault recording.

[0003] Currently, train data recording mainly adopts the application variable recording method. The train data recording system (ERM) in the train network system cannot record all data on the network, and will periodically collect a series of variables pre-configured on the network. These variables are usually important commands and state parameters after screening, such as train speed, traction, driver key, handle level, etc. The collected data will be stored in the non-volatile memory of ERM after simple rearrangement, reorganization or simple calculation processing, forming the corresponding data recording file. This method meets the basic data recording needs of the train to a certain extent, but has many limitations.

[0004] Firstly, the existing recording method is difficult to deal with complex faults. Since only part of the important variables are recorded, when a more complex fault occurs, the recorded data information is often not comprehensive and complete, which makes it difficult for technical personnel to accurately find the problem when analyzing the fault reason using ERM data, seriously restricting the improvement of train performance and reliability. Secondly, the performance bottleneck is highlighted. In terms of data compression, only a single algorithm is used, and it is not optimized for large-scale flow data. When the data volume exceeds 6000 bytes, the compression process is prone to crash, resulting in data loss and affecting the accuracy and integrity of data recording. Thirdly, data processing is complex. Time-sharing data restoration relies on underlying software, and the intermediate analysis process is cumbersome, consuming a lot of time and effort, and the maintenance cost is high. Finally, the storage efficiency is low. Single-stage compression algorithm is difficult to meet the long-time and high-throughput data recording requirements, and it is difficult to adapt to the requirements of full-range recording and in-depth analysis of train data by railway operators. SUMMARY

[0005] According to the technical problem of low storage efficiency of the existing train data recording system, a train data recording system with full-network recording capability is provided. The train data recording system with full-network recording capability mainly uses a new technical scheme based on flow data binary recording, and realizes high-throughput and high-reliability data recording and analysis by means of compression algorithm, flexible port configuration and time-sharing data dynamic restoration technology, thereby completely avoiding the occurrence of compression dead machine problem and significantly reducing operation and maintenance cost.

[0006] The technical means adopted by the present application are as follows: The train data recording system with full-network recording capability comprises: A bottom function module is used to realize full-amount collection, periodic recording and compression of train network data, and provide compressed binary stream data file for the intermediate layer module; An intermediate layer module is used to decompress, format convert and restore time-sharing data of the compressed binary stream file, and generate structured CSV data file for the data analysis adaptation module; A data analysis adaptation module is used to analyze and adapt the structured CSV data file, extract key information and generate standardized data interface for train operation analysis.

[0007] Further, the bottom function module comprises: A periodic data recording submodule; the periodic data recording submodule constructs a recording mechanism based on a Vxworks system, distinguishes different port data through MVB port number or Ethernet ComID, realizes full-amount data collection, and provides multi-dimensional identification for subsequent retrieval; A data compression submodule; the data compression submodule adopts a compression mechanism, and the compression mechanism is as follows: A large amount of data in a single period is stored as a binary file, a compression format file is generated every 2 hours, and a new binary file is recorded synchronously; A fault recording submodule; the fault recording submodule completes signal monitoring through the Vxworks system, records multiple fault bits, and adopts specific bytes and time-sharing transmission structure to identify fault triggering and elimination state by Boolean quantity.

[0008] Further, the recording mode of the periodic data recording submodule comprises: Ethernet priority recording mode, single Ethernet recording mode, single MVB recording mode and double-network recording mode.

[0009] Further, the intermediate layer module comprises: Data processing submodule; the data processing submodule integrates a decompression and format conversion unit, the data processing submodule parses a binary stream file into a CSV format and arranges according to rules; the data processing submodule includes a time-sharing data restoration unit, the time-sharing data restoration unit rearranges data based on a time-sharing identifier, and reserves storage space; Storage mode configuration submodule; the output mode of the storage mode configuration submodule is as follows: the data is merged and decompressed into a single CSV file or stored as an independent CSV file.

[0010] Further, the data parsing adaptation module includes: Data format adaptation submodule; the data format adaptation submodule is compatible with the CSV file generated by the intermediate layer module, and records according to the recording rule of one column per 4 bytes; Parsing configuration submodule; the parsing configuration submodule defines column data parsing rules through a parsing configuration file, and extracts key information based on column positions.

[0011] Further, the specific byte and the time-sharing transmission structure are 28 bytes and 6 time-sharing transmission structures.

[0012] Compared with the prior art, the present application has the following advantages: The present application adopts a flow data binary recording mode and a compression algorithm, effectively solves the problem of ERM freezing under large data, improves the efficiency of data recording and compression, and ensures stable operation of the system.

[0013] The variable alignment processing, time-sharing data restoration and dimension calculation function of the intermediate software of the present application ensure the accuracy and integrity of the data, and provide a reliable data basis for train operation data analysis.

[0014] The rich configuration options of the bottom software and the intermediate software of the present application meet the diversified needs of different train operation scenarios and users, and improve the universality and adaptability of the system.

[0015] The present application is adapted to the existing DAC parsing rules, ensures smooth flow of data in the entire recording, processing and analysis process, and reduces the system upgrade and maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0017] Figure 1The method flowchart of the present application. DETAILED DESCRIPTION

[0018] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0019] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The description of the at least one exemplary embodiment is actually only illustrative, but not intended to limit the present application and its application or use in any way. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0020] It should be noted that the terms used herein are only intended to describe specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and it should also be understood that when the terms "comprise" and / or "include" are used in the specification, there is a reference to the presence of a feature, step, operation, device, component and / or combinations thereof.

[0021] Unless specifically stated otherwise, the relative arrangement of components and steps, numerical expressions, and numerical values set forth in the various embodiments described herein are not limiting. It should be understood that the various parts shown in the drawings are not necessarily drawn to scale in proportion. Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail, but should be considered as part of the specification where appropriate. In all examples shown and discussed herein, any specific value should be interpreted as merely exemplary, and not as a limitation. Therefore, other examples of the exemplary embodiments can have different values. It should be noted that similar reference numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0022] The core objective of the present application is to effectively solve the performance bottleneck problem faced by the traditional ERM in the large-scale data recording process. By providing a brand-new technical solution based on stream data binary recording, with the help of compression algorithm, flexible port configuration and time-sharing data dynamic restoration technology, high-throughput and high-reliability data recording and analysis are realized, the compression deadlock problem is completely avoided, and the operation and maintenance cost is significantly reduced.

[0023] The ERM with full network recording capability has a configurable recording period of 100-1000 ms in periodic data recording, and a throughput capacity of 17000 bytes / period. The ERM has the capability of simultaneous recording on MVB and Ethernet.

[0024] As shown in Figure 1 The present application provides a train data recording system with full network recording capability, comprising: A bottom layer function module for realizing full acquisition, periodic recording and compression of train network data, and providing a binary stream data file after compression for an intermediate layer module; An intermediate layer module for decompressing, format converting and time-sharing data restoring the binary stream file after compression, and generating a structured CSV data file for a data analysis and adaptation module; A data analysis and adaptation module for analyzing and adapting the structured CSV data file, extracting key information and generating a standardized data interface for train operation analysis.

[0025] The bottom layer function module comprises: A periodic data recording submodule; a recording mechanism based on Vxworks system, differentiating data of different ports through MVB port number or Ethernet ComID, realizing full data acquisition and providing multi-dimensional identification for subsequent retrieval. Configuration unit 1 supports recording period (100-1000 ms), flexible setting of recording port, integrated Ethernet priority / single Ethernet / single MVB / dual network recording mode switching function, and adapts to data acquisition requirements of different train operation scenarios A data compression submodule; first, 17000 bytes of single-period data are compressed into a.zhs file, and compression is triggered every 2 hours to generate a lzm file compatible with the original ERM compression algorithm, and a new.zhs file recording is simultaneously started, realizing balance between storage efficiency improvement and format compatibility.

[0026] A fault recording submodule; the original fault recording logic is followed, signal monitoring is completed through Vxworks system, and 1344 fault bits are recorded (using 28 bytes, 6 time-sharing transmission structure, and Boolean quantity indicating fault triggering / elimination state). The time-sharing transmission structure is that multiple faults are transmitted in a bit, and different faults are transmitted in the same bit of different data frames, and a time-sharing identifier in the data frame is used to identify which fault is transmitted in a frame.

[0027] The intermediate layer module comprises: Data processing sub-module; integrated decompression and format conversion unit, which can parse binary stream file into CSV (.bin) format, arrange every 4 bytes as a column (total 4250 columns) according to offset 0-16999, facilitate data structure arrangement. Design time-sharing data restoration unit based on time-sharing flag to realize data rearrangement (position can be configured), this function in the original Vxworks is migrated to the middle layer, reduce the bottom load. Reserve 6380 bytes of storage space (from 18000 bytes start), guarantee the accuracy of time-sharing data processing. Time-sharing data restoration unit is as follows: a bit of fault data in a certain data frame, for b time-sharing data transmission, expand the a bit of transmission data to a*b bit storage space, indicating a*b kinds of faults) Storage mode configuration sub-module; support two output modes: single file mode: merge MVB and Ethernet data into a single CSV file (retain original format); double file mode: store two types of data as independent CSV files to meet differentiated analysis needs.

[0028] The data parsing adaptation module comprises: Data format adaptation sub-module; data format adaptation unit compatible with CSV format generated by the middle layer, adopts the recording rule of every 4 bytes as a column, ensures that column data and parsing logic correspond one by one.

[0029] Parsing configuration sub-module; define column data parsing rules through parsing configuration file, extract key information (such as device status, fault code, etc.) based on column position, provide standardized data interface for train operation analysis. Configuration information is stored in the configuration file, which is used to parse what meaning a certain bit in the CSV file represents, such as the second bit representing traction motor fault, the third bit representing converter over-temperature, etc.

[0030] Embodiment A train data recording system with full-network recording capability, the bottom layer module of the train ERM runs in the on-board recording unit, the recorded file is transmitted to the ground PC through Ethernet or wireless, the middle layer and the data parsing adaptation module run in the ground PC, specifically comprising: ERM bottom layer recording module implementation; Deploy the bottom layer function unit based on Vxworks system in the train ERM device, complete parameter initialization according to "MVB Ethernet configuration table" and "flow recording configuration table".

[0031] After starting recording, real-time acquisition of MVB / Ethernet flow data, compression generation.zhs file according to the set period, automatic conversion into.zhs.lzm file every 2 hours and storage to the on-board storage device. Synchronously monitor the fault signal, record the fault bit state according to the rule of 28 bytes / 6 time-sharing.

[0032] Middle layer module implementation; In the ground data processing equipment, the intermediate layer function unit is run, and the binary stream file of the vehicle-mounted storage equipment is read.

[0033] The original data is generated by the decompression module, processed into a CSV (.bin) file through the format conversion unit, and combined with the CSV data offset table and the variable alignment configuration table to complete data alignment and time restoration.

[0034] According to the user configuration, the processed data is output as a single file (including double network data) or double independent files (MVB / Ethernet separation).

[0035] The data analysis and adaptation module is implemented. The CSV file generated by the intermediate layer is imported into the DAC system, the DAC analysis configuration file is called through the analysis and adaptation module, and the data is analyzed according to the column position.

[0036] The extracted information includes but is not limited to train equipment state, fault trigger timing, etc., providing data support for fault diagnosis and operation optimization.

[0037] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and are not limited thereto; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A train data recording system with full network recording capability, characterized in that, include: The underlying functional modules are used to realize the full collection, periodic recording and compression of train network data, and provide compressed binary stream data files for the intermediate layer modules; The intermediate layer module is used to decompress the compressed binary stream file, convert its format, and restore the time-sharing data, and to generate a structured CSV data file for the data parsing and adaptation module; The data parsing and adaptation module is used to parse and adapt structured CSV data files, extract key information, and generate standardized data interfaces for use in train operation analysis.

2. The train data recording system with full network recording capability according to claim 1, characterized in that, The underlying functional modules include: Periodic data recording submodule; The periodic data recording submodule is based on the Vxworks system to build a recording mechanism, and distinguishes data from different ports by MVB port number or Ethernet ComID to achieve full data collection. The periodic data recording submodule provides multi-dimensional identification for subsequent retrieval. A data compression submodule; the data compression submodule employs a compression mechanism, which is as follows: A large amount of data in a single cycle is stored as a binary file, compressed every 2 hours to generate a compressed file of a different format, and a new binary file is started simultaneously. The fault recording submodule performs signal monitoring through the Vxworks system and records multiple fault bits. The fault recording submodule adopts a specific byte and time-division transmission structure and uses Boolean values ​​to identify the fault triggering and elimination status.

3. The train data recording system with full network recording capability according to claim 2, characterized in that, The recording modes of the periodic data recording submodule include: Ethernet priority recording mode, single Ethernet recording mode, single MVB recording mode, and dual network recording mode.

4. The train data recording system with full network recording capability according to claim 1, characterized in that, The intermediate layer module includes: The data processing submodule integrates a decompression and format conversion unit, which parses the binary stream file into CSV format and arranges it according to rules. The data processing submodule includes a time-sharing data restoration unit, which rearranges the data based on time-sharing identifiers and reserves storage space. The storage mode configuration submodule has the following output modes: merging and decompressing data into a single CSV file or storing them separately as independent CSV files.

5. The train data recording system with full network recording capability according to claim 1, characterized in that, The data parsing and adaptation module includes: Data format adaptation submodule; the data format adaptation submodule is compatible with CSV files generated by the intermediate layer module, and records data using a record rule of one column per 4 bytes; The configuration parsing submodule defines column data parsing rules by parsing the configuration file and extracts key information based on column positions.

6. The train data recording system with full network recording capability according to claim 2, characterized in that, The specific byte and time-division transmission structure is a 28-byte, 6-time-division transmission structure.

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