Data management device, data management system and data management method

Abstract

To enable efficient use of various information.SOLUTION: A data management device is provided, comprising a computation device configured to store state information indicative of states regarding an analysis target, and a storage device configured to receive a data provision request partially specifying a structure contributing to identification of state information, search for and provide state information matching the partial specification from state information in different formats, accept editing of the provided state information, and generate standardized data processing for the analysis target on the basis of the data provision request and a content of the editing.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a data management device, a data management system, and a data management method. [Background technology]

[0002] Conventionally, a technology for ensuring the safe operation of trains is described in Japanese Patent Laid-Open Publication No. 2011-31711 (Patent Document 1). This publication states that "an advanced train safety control system that aggregates information on board includes a train having a control processing device with a storage device that stores geospatial information within a line section, an on-board observation device, and a real-time train position and speed measurement device, ground observation devices within the line section, a control center, and an external organization that provides weather information and earthquake information, and by combining ground observation data from the ground observation devices, on-board observation data from the on-board observation devices, vehicle position and speed information from the real-time train position and speed measurement device, the geospatial information, weather information and earthquake information from the external organization, and observation data obtained from other trains and transmitted from the control center, highly accurate disaster predictions are performed on the train." [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-31711 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology aggregates information collected on the ground and from other vehicles on board, allowing for disaster prediction and reflection in operational control. However, this technology does not take into consideration the diversity of information that can be collected or the diverse ways in which the collected information can be used.

[0005] For example, when collecting vehicle location information, it is possible to use information on travel distance from speed pulses, location information from the Global Positioning System (GPS), location information based on door-opening events, etc. However, because these types of location information vary in format and reliability, it has been an important challenge to find a way to handle them simply, regardless of the format. Furthermore, location information is used for a variety of purposes and applications, such as operation control, fault response, and analysis, and the requirements for the amount of data and reliability vary depending on the purpose and application.

[0006] When performing analysis using a variety of types of data with different reliability in a comprehensive manner, it has been difficult to automate the process of matching and combining the data. Here, we have used the example of analyzing information about railway operations, but this type of problem occurs widely when the data being analyzed contains a variety of data with different levels of reliability.

[0007] Therefore, an object of the present invention is to realize efficient use of a variety of information. [Means for solving the problem]

[0008] In order to achieve the above-mentioned object, one of the representative data management devices and data management systems of the present invention comprises an arithmetic unit and a memory device, the memory device stores status information indicating the status of the object of analysis, the arithmetic unit accepts a data provision request that partially specifies a structure that contributes to identifying the status information, searches for and provides status information that matches the partial specification from status information of different formats, accepts edits to the provided status information, and generates standardized data processing for the object of analysis based on the data provision request and the content of the edits. Furthermore, one representative data management method of the present invention is characterized in that it includes the steps of a data management device storing status information indicating the status of an object of analysis, accepting a data provision request that partially specifies a structure that contributes to identifying the status information, searching for status information that matches the partial specification from status information of different formats, providing the results of the search, accepting edits to the provided status information, and generating standardized data processing for the object of analysis based on the data provision request and the content of the edits. [Effects of the Invention]

[0009] According to the present invention, it is possible to realize efficient use of a variety of information. Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is an explanatory diagram of the configuration of a railway operation data management system according to a first embodiment. [Figure 2] FIG. 10 is an explanatory diagram of input of a data provision request. [Figure 3] Diagram of editing data (part 1). [Figure 4] Diagram explaining data editing (part 2). [Figure 5] Diagram of editing data (part 3). [Figure 6] Diagram of data editing (part 4). [Figure 7] Diagram of editing data (part 5). [Figure 8] Diagram explaining data editing (part 6). [Figure 9] Diagram of data editing (part 7). [Figure 10] Diagram explaining data editing (part 8). [Figure 11] 10 is a specific example of a data request packet. [Figure 12] A concrete example of a dimensional integration table. [Figure 13] 10 is a flowchart for generating a data search edit query linked to an edit screen. [Figure 14] An illustration of railway topology. [Figure 15] FIG. 10 is an explanatory diagram of node errors. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the first embodiment, a railway operation data management system that analyzes various data related to railway operation will be described. In this specification and drawings, components having substantially the same functions or configurations are denoted by the same reference numerals, and redundant explanations will be omitted. [Example]

[0012] FIG. 1 is an explanatory diagram of the configuration of a railway operation data management system. The railway operation data management system includes a user terminal 1 and a server system 2 as a data management device. The user terminal 1 is a computer equipped with an internal CPU (Central Processing Unit) 1-3 and a main memory device 1-4, and is connected to peripheral devices such as a display device 1-1 and a disk 1-2 serving as an auxiliary memory device. The user terminal 1 accepts an operation by the user 9 and transmits a data provision request to the server system 2. Then, the user terminal 1 receives a data response from the server system 2 and causes the display device 1-1 to display the response.

[0013] The server system 2 includes one or more servers 3 and one or more storages 5 . The storage 5 is a storage device that stores status information indicating the status of railway operations, and can also store search logs. The configuration of the server 3 will be described below using a server 3-a, which is one of one or more servers 3, as an example. The server 3 has a CPU 3-1, which is a computing device, a memory 3-2, which is a main storage device, a network interface card (NIC) 3-3, a disk controller 3-4, and a disk 3-5, which is an auxiliary storage device.

[0014] The CPU 3-1 loads programs and data into the memory 3-2 and executes the programs sequentially to realize various functions. Specifically, the memory 3-2 stores data related to the OS (Operating System) 3-11, the use case threshold table 3-12, the status management function 3-13, the dimension integration table 3-14, the reliability analysis function 3-15, the railway topology management table 3-21, the timetable information 3-22, the matter management dictionary 3-23, the search target and the valid width for the width candidate dictionary 3-24, and the extracted know-how data after superimposition 3-25.

[0015] OS3-11 is a group of programs that control the basic operations of the server 3. Table 3-12 for thresholds for use cases is a table that associates the thresholds of data required for each use case. The state management function 3-13 is a function that compares a plurality of state information items acquired in different formats for the same state, and determines an error that occurs in the state information as an error that occurs. The dimension integration table 3-14 is a table that associates layered data acquisition purposes, data tables of status information corresponding to search results, and errors that occur in the status information.

[0016] The reliability analysis function 3-15 is a function for analyzing the reliability of a plurality of pieces of status information acquired in different formats regarding the same status. For example, when the location information of a railway vehicle is acquired based on the opening and closing of the doors of the railway vehicle, travel distance information based on speed pulses, and GPS location information, the reliability analysis function 3-15 increases the reliability of the location information based on the event when a door opening event occurs, decreases the reliability of the travel distance information when the traveling speed is below a predetermined level, and changes the reliability of the GPS location information according to the map information.Then, the reliability corresponding to each piece of location information is integrated to determine the reliability of the final location information. If the data provision request specifies a tolerance, the reliability analysis function 3-15 further provides the result of comparing the actual error with the tolerance together with the status information of the search results.

[0017] The railway topology management table 3-21 is a table for managing the network topology that indicates the network configuration of the railway. The timetable information 3-22 is data showing the operation status of the railway. The event management dictionary 3-23 is data that associates the event to be analyzed with the status information at the time of occurrence of the event. The valid range 3-24 for the search target and width candidate dictionary is determined as the size of a valid range when searching for data using know-how data 3-25 and using a specified range of search results. This valid range is determined from the editing history of the data included in the search log. The extracted and overlaid know-how data 3-25 is generated from the search log as know-how for data acquisition and editing. The search log contains the specified search conditions, search results, the time required for the search, and the edits made to the search results. The railway operation data management system generates a query from the search log that can be used for searches and edits at other times. This query is standardized data processing for the analysis target. By using this query, searches and edits made by experts in the past can be used for searches and edits at any time thereafter.

[0018] 2 is an explanatory diagram of inputting a data provision request. The display device 1-1 of the user terminal 1 displays an input area for the data provision request shown in FIG.

[0019] The input area includes a data type, a target data identifier, an error value, a search condition content, and a search condition specification box. The data type is the type of data used to search for data editing information.

[0020] The data types correspond to the railway topology management table 3-21, timetable information 3-22, matter management dictionary 3-23, search target and valid width for the width candidate dictionary 3-24, and extracted know-how data after superimposition 3-25 shown in Figure 1.

[0021] The target data identifier indicates how the target data is identified. For example, in railway topology management tables and timetable information, the storage location of the target data is indicated by its position in the hierarchical structure, such as "column / nested data / structure identifier / target identifier." In event information, an identifier assigned in advance to the type of event is used. Within a reasonable range, it can be identified by time or distance traveled. The know-how data after overlay is generated from past search logs, and has not yet been generated in the state shown in Figure 2.

[0022] The error value indicates the range between the maximum and minimum values ​​allowed for that item. In Figure 2, the error value is shown as "none" for the railway topology management table and timetable information. It is also shown as "serious failure" for the event information. The appropriate range for time is shown as "60 minutes." The appropriate range for kilometers is shown as "0.2 m."

[0023] In the search condition content, you can enter specific search conditions for the value of that data type. In the search condition specification box, you can specify whether or not to use that data type as a search condition. Note that the data types shown in Figure 2 are just examples, and more can be added as needed. For this reason, a button for adding conditions is also provided in the input area.

[0024] 3 to 10 are explanatory diagrams of editing of data provided in response to a data provision request. In Fig. 3, the display device 1-1 displays two different types of data relating to distance and position as a result of the data provision request.

[0025] One type of data is distance traveled information, or kilometers, from speed pulses. When the train's speed is below 3 km / m, it is difficult to accurately obtain speed pulses based on the rotation of the wheels, so kilometers become less reliable. However, when the train's speed is above 3 km / m, kilometers become more reliable.

[0026] The other data is the event when a train door opens at a station, known as a door-opening event. Door-opening events occur only when the train's position is accurately aligned with the platform, making them more reliable than kilometers. However, because door-opening events occur precisely on the station platform, they cannot provide position information while the train is traveling between stations.

[0027] 3, the screen 1-1-1 of the display device 1-1 displays a data editing layer addition area 1-1-1a and a cursor 1-1-1b. The screen 1-1-1 also displays the following editing screen buttons: a layer deletion button 1-1-1c, a scale adjustment button 1-1-1d, a layer alignment button 1-1-1e, a cutting plane condition setting button 1-1-1f, and a horizontal axis selection button 1-1-1g.

[0028] The data editing layer addition area 1-1-1a in Figure 3 shows the data type, target data identifier, error value, data and reliability, and usability determination for kilometer distance data and door-opening event data. The usability determination is a checkbox that selects whether or not to use the data. When a checkbox is turned on using the cursor, a layer is assigned to the corresponding data, allowing the data to be overlaid. Figure 3 shows a state in which layer 1 is assigned to kilometer distance data and layer 2 is assigned to door-opening events.

[0029] In FIG. 4, the layer alignment button 1-1-1e has been operated, and time has been specified on the horizontal axis of layer 1. The following information is also displayed as properties: The layer "Layer 1" is selected and slides along the time axis. - Difference between edit screen time scale and data time: "-300msec" -The time error range for each layer is +-10 seconds

[0030] In FIG. 5, the layer alignment button 1-1-1e has been operated, and kilometers have been specified on the horizontal axis of Layer 1. The following information is also displayed as properties: The layer "Layer 1" is selected and slides along the time axis. - Difference between kilometer scale and data on the editing screen: "-30m" - Error range of kilometres for each layer: 100m

[0031] In FIG. 6, the cut plane condition setting button 1-1-1f is operated, and the cursor 1-1-1b is used to select the reliability of layer 1. The following information is then displayed as properties: The layer "Layer 1" is selected and slides along the time axis. Intercepts to be removed based on reliability: "less than 80%" Note that the cut surface condition save button 1-1-1h is also displayed here, but details of this button will be described later. Furthermore, at the stage shown in Figure 6, input is not accepted for the intercepts to be removed based on the data values.

[0032] In Fig. 7, the cutting plane condition setting button 1-1-1f is operated, and the cursor 1-1-1b is used to select the data of layer 1. The following information is displayed as properties: The layer "Layer 1" is selected and slides along the time axis. - Intercept "less than 30" to be removed based on the data value - Intercept "above XXX" to be removed based on the value on the horizontal axis

[0033] Figure 8 shows the results of extracting data from layer 1 based on the reliability and data surface values. The following information is displayed as properties: The layer "Layer 1" is selected and slides along the time axis. Intercepts to be removed based on reliability: "less than 80%" - Intercepts to be removed based on data values: "less than 30%" The property here indicates the cutting plane condition used to cut out the data of the currently displayed layer 1.

[0034] In Fig. 9, the scale adjustment button 1-1-1d is operated, and the cursor 1-1-1b is used to change the scale of the data in layer 2. The following information is also displayed as properties: Layer 2 is the selected layer that is being slid along the time axis. Horizontal scale ratio: -8% In this state, if the cutting plane condition save button 1-1-1h is operated, the state will change to that shown in FIG.

[0035] In Figure 10, screen 1-1-1 displays the data of Layer 1 and Layer 2 after applying the cutting plane condition settings and scale adjustment. Also, as properties, various conditions, an input form for know-how names, and a button 1-1-1j for automatically converting data search and editing queries from editing conditions are displayed.

[0036] The various conditions include conditions for the period, conditions for the cut surface for each layer, and valid width. These are displayed based on the editing operations performed up to that point. Furthermore, these conditions can be added, deleted, or modified as appropriate. In this way, by adding, deleting, or modifying conditions based on the actual search and editing results, a formulated data processing query that can be applied to subsequent analysis can be generated. By using this query, it is possible to easily perform data processing similar to that performed by an expert, and in a sense, the expert's know-how is converted into a query.

[0037] The know-how name is a name given to the formulated data processing. This name may be given arbitrarily or may be generated based on the content of the data processing. After entering various conditions and the know-how name, when the "Data Search Edit Query Automatic Conversion from Editing Conditions" button 1-1-1j is operated, the server 3 automatically generates a query.

[0038] The server 3 stores the generated query as the extracted superimposed know-how data 3-25 and displays it as a candidate for subsequent analysis. When this query is selected, the server 3 generates a data provision request similar to the data provision request packet and performs editing on the corresponding data.

[0039] Figure 11 shows a specific example of a data provision request packet. Figure 11 shows an example of packet 1-1-2a that specifies an event interval and a multidimensional tolerance range, and packet 1-1-2b that specifies a time interval and a multidimensional tolerance range. These packets include elements for sending information to the server 3 that narrows down the data, such as the data analysis purpose, multidimensional tolerance, and intervals based on time or events.

[0040] For example, in packet 1-1-2a, the data analysis purpose is "Past history analysis / Stop time / Stop time analysis / Operation efficiency." Also, the multidimensional tolerance is specified as "'* / Kilometers', value: '0', unit 'm'." By using the wildcard "*" to ignore part of the data, it is possible to search for data in "Kilometers" with the unit "m" from a database stored in any data format. Note that kilometers indicates the distance traveled calculated based on the speed pulse.

[0041] Also, packet 1-1-2a specifies the events from "Control Event / Door Open" to "Control Event / Door Close", while packet 1-1-2b specifies the time from "2021-01-01T00:00:00" to "2021-01-01T00:01:00". In this way, you can perform a search by specifying the search range period in any format.

[0042] Figure 12 shows a specific example of a dimension integration table. The dimension integration table 3-14 is a management table that includes data acquisition objectives (user input values) hierarchically organized into at least one level, data templates on the server 3 and their learning status, data tables, target data identifiers, and error values ​​that occur for comparison with allowable errors, and is used for analysis objectives and automatic learning of target data.

[0043] FIG. 13 is a flowchart for generating a data search and editing query linked to the editing screen. First, the server 3 narrows down the type and range of data that takes advantage of the characteristics of railways (step S100). This narrowing down can be done using identifiers that indicate the storage location of the data or the structure of the data itself. In particular, narrowing down the type and range of data that takes advantage of the characteristics of railways involves identifying data based on railway topology and timetable information. Another characteristic of railways is that they use a wide variety of information that differs in format and reliability but can be combined. By appropriately selecting, editing, and combining such information, the desired data can be obtained.

[0044] That is, prior to step S100, there is a step of storing state information indicating the state of the analysis target. Step S100 includes the steps of accepting a data provision request that partially specifies a structure that contributes to identifying the state information, searching for state information that matches the partial specification from state information of different formats, and providing the results of the search.

[0045] After step S100, the server 3 extracts and connects valuable data by editing the reliability and values ​​on the editing screen (step S101). That is, step S101 includes a step of accepting edits to the provided state information.

[0046] After step S101, the server 3 automatically converts the processing conditions in the editing work into a data search editing query (step S102). This step S102 is a step of generating standardized data processing for the analysis target based on the data provision request and the editing content.

[0047] Figure 14 is an explanatory diagram of railway topology. As shown in Figure 14, stations, tracks, signaling equipment, and other aspects of a line can be managed as nodes and links in the topology. Information such as distance on the line can be managed by assigning various coordinate systems to the nodes in the topology.

[0048] Furthermore, by providing a table that associates information types, states, and occurring errors with topology nodes and links, it is possible to comprehensively manage the operating status of railways.

[0049] Figure 15 is an explanatory diagram of node errors. Nodes in railway topology may form a hierarchy. For example, in Figure 15, Platform Edge (the boundary between track and railway line), Platform (track), Operational Point (station), and Railway Line (route) form a hierarchy.

[0050] In cases where upper-level nodes aggregate information from lower-level nodes and provide it to Server 3, errors increase when aggregating the information, lowering reliability. However, Server 3 combines information with different reliability levels, allowing it to effectively utilize information with low reliability.

[0051] As described above, according to the disclosed embodiment, a railway operation data management system including a server 3 as a data management device comprises a CPU 3-1 as a calculation device and a storage 5 as a memory device, the memory device stores status information indicating the status of the object of analysis, the calculation device accepts a data provision request that partially specifies a structure that contributes to identifying the status information, searches for and provides status information that matches the partial specification from status information of different formats, accepts edits to the provided status information, and generates standardized data processing for the object of analysis based on the data provision request and the content of the edits. Therefore, the data management device and data management system can realize efficient use of a variety of information. Specifically, it is possible to visually compare data with varying reliability, automatically match information with conditional processing, and manage the combined results and objectives. Furthermore, automated query generation can be achieved, including the recognition of state (static and dynamic event information) and its conversion to a reasonable time range for analysis. It also provides functions and screens that allow editing of multi-level linkages based on events (static and dynamic event information), and can automatically generate a know-how set of many related query conversion rules from these operation logs.

[0052] Furthermore, the analysis target is railway operations, and the computing device generates standardized data processing based on the data provision request, the content of the editing, and the network configuration of the railway. This makes it possible to utilize the unique characteristics of railways and realize efficient use of a variety of information related to railway operations.

[0053] Moreover, the editing is, for example, superimposing a plurality of pieces of status information. Moreover, the editing is, for example, editing in which the state information is extracted based on a value or reliability. In this way, by standardizing the editing process of extracting and superimposing multiple pieces of state information based on their values ​​and reliability, it becomes possible to widely utilize the know-how of experts.

[0054] The present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, not only can the configurations be deleted, but also replacements and additions of configurations are possible.

[0055] For example, know-how data, which is a collection of search and editing queries, can be registered and generated when actual operations are performed, or it can be generated later at any time by accumulating search and editing logs. Furthermore, although the normal embodiment has been described using railway operations as an example, the present invention can be applied to any system that analyzes a variety of data. [Explanation of symbols]

[0056] 1: User terminal, 1-1: Display device, 1-1-1: Screen, 1-1-2: Packet, 1-2: Disk, 1-4: Main memory device, 2: Server system, 3: Server, 3-1: CPU, 3-2: Memory, 3-4: Disk controller, 3-5: Disk, 3-12: Threshold table for use cases, 3-13: Status management function, 3-14: Dimension integration table, 3-15: Reliability analysis function, 3-21: Railway topology management table, 3-22: Timetable information, 3-23: Event management dictionary, 3-24: Validity range, 3-25: Know-how data, 3-26: Know-how search function for each event received, 3-27: Prefetch condition table, 5: Storage, 9: User

Claims

1. A computing device; a storage device; the storage device stores status information indicating a status of an analysis target; The computing device accepting a data provision request that partially specifies a structure that contributes to identifying the state information, and searching for and providing state information that matches the partial specification from state information of different formats; Accept edits to the status information you provide, generating a standardized data processing for the analysis target based on the data provision request and the content of the edit; The analysis target is railway operations, The editing of the state information is the extraction and splicing of valuable data by editing reliability and values; The generation of the standardized data processing for the analysis target generates a query from the search log that can be used for searching and editing at other times. A data management device characterized by:

2. 2. The data management device according to claim 1, The data management device is characterized in that the calculation device generates standardized data processing based on the data provision request, the content of the editing, and the railway network configuration.

3. 2. The data management device according to claim 1, The data management device is characterized in that the editing is superimposition of a plurality of pieces of state information.

4. 4. The data management device according to claim 3, The data management device is characterized in that the editing is editing in which the state information is extracted based on a value or reliability.

5. 2. The data management device according to claim 1, the status information includes information indicating a running status of the railway vehicle, The data management device is characterized in that the standardized data processing is processing for acquiring information related to control of the running of the railway vehicle.

6. 6. The data management device according to claim 5, The data management device is characterized in that the arithmetic device uses the standardized data processing to obtain necessary information in advance depending on the running state of the railway vehicle.

7. A computing device; a storage device; the storage device stores status information indicating a status of an analysis target; The computing device accepting a data provision request that partially specifies a structure that contributes to identifying the state information, and searching for and providing state information that matches the partial specification from state information of different formats; Accept edits to the status information you provide, generating a standardized data processing for the analysis target based on the data provision request and the content of the edit; The analysis target is railway operations, The editing of the state information is the extraction and splicing of valuable data by editing reliability and values; The generation of the standardized data processing for the analysis target generates a query from the search log that can be used for searching and editing at other times. A data management system comprising:

8. The data management device storing status information indicating a status related to the analysis target; receiving a data provision request that partially specifies a structure that contributes to identifying the state information; searching for status information that matches the partial specification from among status information of different formats; providing results of said search; accepting edits to the provided status information; generating a standardized data process for the analysis target based on the data provision request and the content of the edit; Including, The analysis target is railway operations, The editing of the state information is the extraction and splicing of valuable data by editing reliability and values; The generation of the standardized data processing for the analysis target generates a query from the search log that can be used for searching and editing at other times. A data management method comprising:

Images