JP7809993B2 - Information processing system, information processing method, and information processing program - Google Patents

Description

The present invention relates to an information processing system, an information processing method, and an information processing program.

When analyzing data, it is common to use graphs to visually represent it. When there is a large amount of data in question, a method is used in which only a portion of the data is represented as a graph, depending on resources, for example.

For example, Japanese Patent Application Laid-Open No. 2013-246500 (Patent Document 1) discloses technology for processing time series data to display multiple pieces of time series data on a display screen in multiple stages with a time granularity that is the display time per dot on the display.

JP 2013-246500 A

The methods disclosed in the prior art select the data to be displayed according to a predetermined algorithm without considering the meaning of the data. As a result, there is a high possibility that the data that is actually desired to be displayed will not be displayed in a graph.

Therefore, one objective of the present invention is to provide technology that displays graphs using limited resources while reducing the possibility that information that should be displayed will not be displayed.

An information processing system according to one example of the present invention includes a first data selection unit that selects a smaller number of first data from a plurality of data to be displayed in a graph according to a predetermined first algorithm, a second data selection unit that selects second data from the plurality of data according to predetermined settings, and a graph generation unit that generates a graph based on the first data and the second data. The settings include a second algorithm and parameters for determining that the data should be displayed in a graph.

With this configuration, a smaller number of first data items are used to display the graph according to a predetermined first algorithm from among the multiple data items to be displayed in the graph. This reduces the resources required for processing compared to when all of the multiple data items are used. Furthermore, because the second data items selected according to predetermined settings are also displayed in the graph, the possibility of data that should be displayed in the graph not being displayed is reduced.

The settings may further include criteria for selecting a smaller number of data from the multiple data sets determined to be displayed in the graph. With this configuration, even when there is multiple data sets to display in the graph, the increase in required resources can be suppressed.

The first data selection unit may select one piece of first data for each predetermined number of data, and the second data selection unit may select one piece of second data for each predetermined number of data. With this configuration, one piece of first data and up to one piece of second data are selected for each predetermined number of data, thereby preventing an increase in the number of selected data.

The first data selection unit and the second data selection unit may be configured to decrease the predetermined number of first data and second data items for selection over time. This configuration reduces the resources required for the initial display and allows the user to view more detailed graphs over time.

The graph generation unit may represent the first data in a graph in a first display format, and the second data in a graph in a second display format. With this configuration, the first data and the second data are represented in different display formats, making it easier for the user to distinguish between the data.

The setting parameters may be varied according to at least one of a function and a third algorithm. This configuration allows the settings to be selected as the second data to be adjusted according to the situation.

The information processing system may further include a storage unit that stores at least one of a predefined first preset that can be used as the first algorithm and a predefined second preset that can be used as a setting. With this configuration, even a user with little knowledge can make the necessary settings in a short amount of time.

The information processing system may further include an information processing device that executes a browser application. The graph may be displayed via a browser provided by executing the browser application. With this configuration, the graph can be displayed using a general-purpose browser application.

An information processing method according to another example of the present invention includes the steps of selecting a smaller number of first data from a plurality of data to be displayed in a graph in accordance with a predetermined first algorithm, selecting second data from the plurality of data in accordance with predetermined settings, and generating a graph based on the first data and the second data. The settings include a second algorithm and parameters for determining that the data should be displayed in a graph.

An information processing program according to yet another example of the present invention causes a computer to execute the steps of selecting a smaller number of first data items from a plurality of data items to be displayed in a graph in accordance with a predetermined first algorithm, selecting second data items from the plurality of data items in accordance with predetermined settings, and generating a graph based on the first data items and the second data items. The settings include a second algorithm and parameters for determining that the data should be displayed in a graph.

This invention makes it possible to display graphs using limited resources while reducing the possibility that information that should be displayed will not be displayed.

FIG. 1 is a diagram illustrating an overview of an information processing method executed by an information processing system according to an embodiment of the present invention. 1 is a schematic diagram showing an example of the overall configuration of an information processing system according to the present embodiment; FIG. 2 is a block diagram showing an example of a hardware configuration of a control device of the information processing system according to the present embodiment. FIG. 2 is a block diagram showing an example of a hardware configuration of an information processing device in the information processing system according to the present embodiment. FIG. 1 is a diagram illustrating an example of a graph display according to the related art. FIG. 10 is a diagram showing an example of a graph display according to the present embodiment. FIG. 10 is a schematic diagram showing an example of a user interface screen including a graph display provided by the information processing system according to the present embodiment. FIG. 10 is a schematic diagram showing an example of a user interface screen including a graph display provided by the information processing system according to the present embodiment. FIG. 10 is a schematic diagram showing another example of a user interface screen including a graph display provided by the information processing system according to the present embodiment. FIG. 1 is a schematic diagram showing an example of a functional configuration of an information processing system according to an embodiment of the present invention. FIG. 10 is a schematic diagram showing a modified example of the functional configuration of the information processing system according to the present embodiment. FIG. 10 is a schematic diagram showing another modified example of the functional configuration example of the information processing system according to the present embodiment. 10A and 10B are diagrams illustrating an example in which a plurality of threshold values are set in the important data setting (important data determination setting) according to the present embodiment. FIG. 10 is a diagram for illustrating another example in which a plurality of threshold values are set in the important data setting (important data determination setting) according to the present embodiment. 10A and 10B are diagrams illustrating an example of setting a variable threshold value included in an important data setting (important data determination setting) according to the present embodiment. FIG. 10 is a diagram illustrating another example of setting a variable threshold value included in the important data setting (important data determination setting) according to the present embodiment. FIG. 10 is a diagram illustrating yet another setting example of important data setting (important data determination setting) according to the present embodiment. FIG. 10 is a schematic diagram showing an example of a user interface screen relating to presets provided by the information processing system according to the present embodiment. FIG. 10 is a diagram for explaining the processing load depending on the size of a bucket in the information processing system according to the present embodiment. FIG. 10 is a diagram for illustrating another example of graph display according to the present embodiment. FIG. 10 is a diagram illustrating an example of important data selection setting according to the present embodiment. 10A to 10C are diagrams for explaining an example of how a graph display changes over time according to the present embodiment. 10A to 10C are diagrams for illustrating an example of a display mode of a graph display according to the present embodiment. FIG. 10 is a diagram for illustrating another example of a display mode of a graph display according to the present embodiment. 10A and 10B are diagrams for explaining an example of changing the display granularity of important data according to the present embodiment. 10A and 10B are diagrams for explaining an example of a process for generating representative data according to the present embodiment. FIG. 27 is a diagram for explaining the process of displaying a graph using representative data generated by the process shown in FIG. 26 . 10A and 10B are diagrams for explaining another example of processing for generating representative data according to the present embodiment. FIG. 29 is a diagram for explaining the process of graph display using representative data generated by the process shown in FIG. 28 . 10 is a flowchart showing a processing procedure for displaying a graph in information processing system 1 according to the present embodiment. 10 is a flowchart showing a processing procedure relating to setting in information processing system 1 according to the present embodiment. 32 is a flowchart showing the processing steps of the setting preset creation processing shown in FIG. 31 . 32 is a flowchart showing the processing procedure for creating new settings shown in FIG. 31 . 32 is a flowchart showing the processing steps of a process for creating settings based on the setting preset shown in FIG. 31 .

Embodiments of the present invention will be described in detail with reference to the drawings. Note that identical or equivalent parts in the drawings will be designated by the same reference numerals and their description will not be repeated.

<A. Application example>
First, an example of a situation in which the present invention is applied will be described.

Figure 1 is a diagram illustrating an overview of the information processing method executed by information processing system 1 according to this embodiment.

Referring to FIG. 1, first, assume that multiple pieces of data (raw data 21) are to be displayed in a graph. The information processing system 1 includes a first data selection unit 22 (such as a sampling module 80 described below), a second data selection unit 23 (such as an important data selection module 90 described below), and a graph generation unit 24 (such as a graph generation module 74 described below).

The first data selection unit 22 selects a smaller number of first data 27-1 to 27-3 (sampling data, described below) from the raw data 21 in accordance with a first algorithm. The second data selection unit 23 selects second data 28-1 and 28-2 (important data or selected important data, described below) from the raw data 21 in accordance with predetermined settings.

The graph generation unit 24 generates the graph 26 based on the first data 27-1 to 27-3 and the second data 28-1 and 28-2.

The settings used by the second data selection unit 23 include a second algorithm and parameters (e.g., threshold value 25) for determining which data should be displayed in the graph. In other words, the settings include an algorithm and parameters for selecting data that is important (meaningful) to the user.

<B. Overall configuration example>
Next, an example of the overall configuration of information processing system 1 according to the present embodiment will be described.

Figure 2 is a schematic diagram showing an example of the overall configuration of an information processing system 1 according to this embodiment. Referring to Figure 2, the information processing system 1 according to this embodiment includes, as its main components, a control device 100, an information processing device 200, and an HMI (Human Machine Interface) 300.

The control device 100 may be embodied as a type of computer, such as a programmable logic controller (PLC). The control device 100 is connected to the field device group 10 via a field bus 2. The field bus 2 preferably employs an industrial communication protocol. Known examples of such communication protocols include EtherCAT (registered trademark), EtherNet/IP (registered trademark), DeviceNet (registered trademark), and CompoNet (registered trademark).

The field device group 10 includes devices that collect input data from fields such as production sites, and devices that perform some kind of action on the field based on commands (output data) generated by the control device 100.

In the example configuration shown in Figure 2, the field device group 10 includes a remote I/O (Input/Output) device 12, a relay group 14, a servo driver 18, and a servo motor 20.

The control device 100 has a time-series database that sequentially stores input data from the field device group 10, output data to the field device group 10, and internal data managed within the control device 100, and displays or outputs the raw data stored in the time-series database in the manner described below.

The information processing device 200 accesses the control device 100 and displays the data stored in the control device 100 in a graph. In the configuration example shown in Figure 2, the information processing device 200 is connected to the control device 100 via the upper network 4, but it may be connected to the control device 100 in any manner.

The HMI 300 is connected to the control device 100 via the upper network 4, displays information from the control device 100, and sends commands to the control device 100 in response to user operations.

<C. Hardware Configuration Example>
Next, an example of the hardware configuration of main devices in information processing system 1 according to the present embodiment will be described.

(c1: control device 100)
3 is a block diagram showing an example of a hardware configuration of control device 100 of information processing system 1 according to the present embodiment. Referring to Fig. 3, control device 100 includes a processor 102 such as a CPU (Central Processing Unit) or an MPU (Micro-Processing Unit), a chipset 104, a main memory device 106, a secondary memory device 108, a host network controller 110, a USB (Universal Serial Bus) controller 112, a memory card interface 114, an internal bus controller 122, a field bus controller 120, and I/O units 124-1, 124-2, ...

The processor 102 reads various programs stored in the secondary storage device 108, deploys them in the main storage device 106, and executes them to perform the processing required by the control device 100. The chipset 104 controls data transmission between the processor 102 and each component.

The secondary storage device 108 stores a system program 131, a user program 132 that describes the contents of the control calculations, an application execution environment program 133 that provides an environment for executing any application as described below, and an application program 134 that describes one or more applications.

The upper network controller 110 controls data exchange with other devices via the upper network 4. The USB controller 112 controls data exchange with the information processing device 200 via a USB connection.

The memory card interface 114 is configured to allow a memory card 116 to be attached and detached, making it possible to write data to the memory card 116 and read various data (user programs, trace data, etc.) from the memory card 116.

The internal bus controller 122 is an interface that exchanges data with the I/O units 124-1, 124-2, etc. installed in the control device 100.

The fieldbus controller 120 controls the exchange of data with other devices via fieldbus 2.

Figure 3 shows an example configuration in which the processor 102 executes a program to provide the required functions, but some or all of these provided functions may be implemented using dedicated hardware circuits (e.g., an ASIC (Application Specific Integrated Circuit) or FPGA (Field-Programmable Gate Array)). Alternatively, the main parts of the control device 100 may be realized using hardware that conforms to a general-purpose architecture (e.g., an industrial PC based on a general-purpose PC). In this case, virtualization technology may be used to run multiple operating systems (OS) for different purposes in parallel, and required applications may be executed on each OS.

(c2: information processing device 200)
4 is a block diagram showing an example of a hardware configuration of information processing device 200 in information processing system 1 according to the present embodiment. Information processing device 200 according to the present embodiment is configured, for example, by a general-purpose computer having hardware that complies with a general-purpose architecture.

Referring to FIG. 4, the information processing device 200 includes a processor 202 such as a CPU or a GPU (Graphics Processing Unit), an optical drive 204, a main memory device 206, a secondary memory device 208, a USB controller 212, a network controller 214, an input unit 216, and a display unit 218. These components are connected via a bus 220.

The processor 202 reads various programs stored in the secondary storage device 208, deploys them in the main storage device 206, and executes them to perform any desired processing.

The secondary storage device 208 typically stores an OS 222 and a browser application 224 for accessing the control device 100. The secondary storage device 208 may also store necessary programs other than the programs shown in FIG. 4.

The information processing device 200 has an optical drive 204, and the program stored on a recording medium 205 (for example, an optical recording medium such as a DVD (Digital Versatile Disc)) that non-transiently stores a computer-readable program is read and installed in a secondary storage device 208, etc.

The various programs executed by the information processing device 200 may be installed via a computer-readable recording medium 205, or may be installed by downloading them from a server device on a network. Furthermore, the functions provided by the information processing device 200 according to this embodiment may also be realized by utilizing some of the modules provided by the OS 222.

The USB controller 212 controls data exchange with the control device 100 via a USB connection. The network controller 214 controls data exchange with other devices via any network.

The input unit 216 is composed of a keyboard, mouse, etc., and accepts user operations. The display unit 218 is composed of a display, various indicators, a printer, etc., and outputs processing results from the processor 202.

Figure 4 shows an example configuration in which the necessary functions are provided by the processor 202 executing a program, but some or all of these provided functions may be implemented using dedicated hardware circuits (e.g., ASICs or FPGAs).

(c3:HMI300)
HMI 300 according to the present embodiment is configured, for example, by a general-purpose computer having hardware that conforms to a general-purpose architecture. The basic hardware configuration is similar to that of information processing device 200 shown in FIG. 4, and therefore detailed description thereof will not be repeated.

D. Processing Overview
Next, an overview of the processing in information processing system 1 according to the present embodiment will be described.

Time-series data collected from the field can be enormous in volume. For example, if data is collected at 1-millisecond intervals, the amount of data for one day (24 hours) can be as much as 86.4 million. Graphically displaying all of this collected data requires a lot of resources for graph generation, and processing also takes a lot of time.

As a result, a solution that is often adopted is to use only a portion of the collected data for graph display. This solution employs an algorithm that thins out the data used for graph display while maintaining the shape of the graph as much as possible.

However, the data used for graph display is simply selected according to a predetermined algorithm, without taking into consideration the meaning of the data (for example, whether it is normal or abnormal). As a result, data that is important (meaningful) to the user (hereinafter also referred to as "important data") may not be selected, and the user may miss that important data.

Figure 5 shows an example of a graph display according to related technology. Figure 5 shows an example of displaying a simplified graph by selecting three points from 15 points of raw data. In other words, a simplified display is performed using three points selected from the 15 points. Note that the target raw data includes normal data and abnormal data. Abnormal data is data with values below the threshold value of 30.

In this case, the rate at which data is selected is 3 points / 15 points = 1/5, meaning that 1 in 5 points will be selected for display. Therefore, we will assume a process in which 5 points are grouped together in chronological order into one bucket, and one piece of data is selected to represent each bucket.

In the example shown in Figure 5, the algorithm for selecting a representative point selects the median value of the data contained in each bucket.

In the graph shown in Figure 5, black circles (●) represent selected data (data that will be plotted), and white circles (○) represent data that was not selected (data that will not be plotted). When a simplified display using only three points is performed as shown in Figure 5, the two data marked A and B are both abnormal data, but are shown as white circles and not displayed.

In other words, a user looking at the simplified display shown in Figure 5 would miss the fact that it contains anomalous data.

Figure 6 is a diagram showing an example of a graph display according to this embodiment. Figure 6 shows an example of displaying raw data similar to that shown in Figure 5. In the example shown in Figure 6, in addition to the data displayed in Figure 5, two pieces of data labeled A and B, which are abnormal data, are also displayed.

This embodiment employs a method that combines an algorithm that thins out the data used for graph display while maintaining the shape of the graph as much as possible (e.g., selecting the median value of each bucket) with an algorithm that additionally selects data based on importance.

An example of an algorithm for additionally selecting data based on importance is one that, if important data exists in each bucket, always uses that important data for graph display. In the example shown in Figure 6, the smallest value of abnormal data is (additionally) selected for each bucket (two pieces of data labeled A and B).

In this way, this embodiment employs a simplified display in which only a portion of the target raw data is used for graph display, rather than all of it, while anomalous data and other data that is important to the user is excluded from the data that is omitted.

<E. Terms＞
As used herein, a "graph" encompasses any visual representation of data. In the following description, several types of graphs are illustrated, but the term "graph" is not limited to the illustrated representations and may include graphs of various representational forms.

In this specification, the algorithm (process) that thins out the data used to display a graph while maintaining the shape of the graph as much as possible is collectively referred to as "downsampling." One example of downsampling is an algorithm that selects one sampled piece of data for every predetermined number of pieces of data (every bucket). However, the term "downsampling" is used for convenience and is not limited to processes that select one sample for every predetermined number of samples, but can also include any process that extracts a portion of the entire data. The data selected by downsampling is also referred to as "sampling data." The algorithms and parameters used for downsampling are collectively referred to as "sampling settings."

For example, the set of settings required to select one out of every five points of raw data to display corresponds to "sampling settings," and each selected point corresponds to "sampling data."

In this specification, the algorithms and parameters used to determine whether data is important are collectively referred to as "important data settings." One example of an algorithm used to set important data is an algorithm that selects one piece of sampling data for every predetermined number of pieces of data (every bucket).

Important data settings include algorithms and parameters (e.g., thresholds and corresponding criteria) for determining whether data should be displayed in the graph (i.e., important data). The algorithms and parameters for determining whether data is important are collectively referred to as "important data determination settings."

The important data settings also include algorithms and parameters for selecting at least a portion of the data determined to be important based on the important data settings for graph display. The algorithms and parameters for selecting at least a portion of the important data are collectively referred to as "important data selection settings." Important data selected based on the important data selection settings is referred to as "selected important data." In other words, the important data selection settings further include selection criteria for selecting a smaller number of data from the multiple pieces of data (multiple important data) determined to be displayed in a graph.

For example, the set of settings required for the process of determining that data below a threshold is important data corresponds to the "important data determination settings." The set of settings required for the process of selecting the smallest value data for each bucket from the data determined to be important data corresponds to the "important data selection settings." In the following explanation, when the term "important data settings" is simply used, it can include both the important data determination settings and the important data selection settings.

In addition, in this specification, "data selection" does not only refer to the process of selecting (or extracting) one or more pieces of data from multiple pieces of data, but also includes the process of determining (or generating) representative data (representative data) from multiple pieces of data, as will be described below.

<F. Usage example>
Next, an example of using the graph display according to this embodiment will be described.

Figures 7 and 8 are schematic diagrams showing an example of a user interface screen including a graph display provided by information processing system 1 according to this embodiment. Figures 7 and 8 show an example of displaying a graph of time-series data.

Referring to FIG. 7, the user interface screen 240 includes a graph display area 250, a sampling setting area 260, and an important data setting area 270.

Graphs containing sampled data and selected important data are displayed in the graph display area 250. More specifically, the sampled data is represented in the form of a line graph 252, and the selected important data is represented by cross (x) plots 254.

The graph also displays the threshold value 256 for the important data setting (important data judgment setting).

The sampling setting area 260 includes a pull-down menu 262 for selecting the algorithm used for the sampling setting, and a slider 264 for inputting the parameters used for the sampling setting. Figure 7 shows an example in which 300 buckets are set and one point representing the median value is sampled for each bucket.

The important data setting area 270 includes a pull-down menu 272 for selecting the algorithm used for the important data setting (important data judgment setting), an input field 274 for inputting parameters used for the important data setting (important data judgment setting), a pull-down menu 276 for selecting the judgment criteria used for the important data setting (important data judgment setting), and a pull-down menu 278 for selecting the judgment criteria used for the important data setting (important data selection setting). Figure 7 shows an example in which, for each bucket, data with a value greater than the threshold value 256 is considered important data, and the maximum value of the important data appearing in each bucket is selected as the selected important data.

Figure 8 shows an example of a graph display when the sampling settings have been changed. More specifically, in Figure 8, the number of buckets has been reduced from 300 to 100. As a result, the number of sampled data points has also been reduced, and the line graph 252 has fewer vertices, resulting in a simpler shape.

As the amount of sampled data decreases, the amount of data considered important also decreases, resulting in a decrease in the amount of selected important data.

Figure 9 is a schematic diagram showing another example of a user interface screen including a graph display provided by the information processing system 1 according to the present embodiment. Figure 9 shows an example of displaying a scatter plot graph.

Referring to FIG. 9, the user interface screen 242 includes a graph display area 251, a sampling setting area 260, and an important data setting area 270.

The graph display area 251 displays a scatter plot including sampled data and selected important data. More specifically, the sampled data is represented by a black circle (●) plot 253, and the selected important data is represented by a cross (×) plot 254.

The graph also displays the threshold range 257 for the important data settings (important data judgment settings).

The sampling setting area 260 includes a pull-down menu 262 for selecting the algorithm used for the sampling setting, and a slider 264 for inputting the parameters used for the sampling setting. Figure 9 shows an example in which 300 buckets are set and one point is sampled per bucket using random sampling.

The important data setting area 270 includes a pull-down menu 272 for selecting the algorithm used in the important data setting (important data judgment setting), an input field 275 for inputting multiple parameters used in the important data setting (important data judgment setting), a pull-down menu 276 for selecting the judgment criteria used in the important data setting (important data judgment setting), and a pull-down menu 278 for selecting the judgment criteria used in the important data setting (important data selection setting). Figure 9 shows an example in which data outside the threshold range 257 defined by the four points entered in the input field 275 is considered important data, and the selected important data is selected so as to maximize the area of the polygon defined by the selected important data selected from each bucket.

Note that the user interface screens are not limited to the examples shown in Figures 7 to 9, and any user interface screen may be provided.

<G. Functional configuration example>
Next, an example of a functional configuration of information processing system 1 according to the present embodiment will be described.

Figure 10 is a schematic diagram showing an example functional configuration of an information processing system 1 according to this embodiment. Figure 10 shows a typical example configuration in which the control device 100 is responsible for the main processing, and the user checks graphs on the information processing device 200.

Referring to FIG. 10, the control device 100 includes, as its functional components, a data acquisition module 70, a sampling module 80, a significant data selection module 90, and a graph generation module 74. These functional modules are typically implemented by the processor 102 of the control device 100 executing an application program 134.

The control device 100 also has a setting preset group 50 and a setting group 60. The setting preset group 50 includes a sampling setting preset 52, an important data setting preset 54, and a graph setting preset 56. The setting group 60 includes a sampling setting 62, an important data setting 64, and a graph setting 66.

The data acquisition module 70 acquires raw data 72 from the control device 100's time series database, etc. The data acquisition module 70 can also add newly acquired data to the raw data 72.

The sampling module 80 selects a smaller number of data items from the multiple data items to be displayed in a graph according to a predetermined algorithm. More specifically, the sampling module 80 selects sampled data items 82 by downsampling from the raw data 72 based on the sampling settings 62.

The important data selection module 90 selects data from multiple data sets according to predetermined settings. More specifically, the important data selection module 90 extracts important data contained in the raw data 72 based on the important data settings 64, and selects at least a portion of the extracted important data as selected important data 92.

The graph generation module 74 generates a graph based on the data selected by the sampling module 80 and the data selected by the important data selection module 90. More specifically, the graph generation module 74 generates a graph using the sampling data 82 and selected important data 92 based on the graph settings 66. The graph generation module 74 has a display update module 76 for updating the graph over time, as raw data 72 is added, etc.

The sampling setting preset 52 contains one or more predefined settings (algorithms and parameters used for downsampling) that can be used as the sampling setting 62. The important data setting preset 54 contains one or more predefined settings (algorithms and parameters used for the important data determination setting and selection criteria used for the important data selection setting) that can be used as the important data setting 64. The graph setting preset 56 contains one or more predefined settings that can be used as the graph setting 66.

Note that it is not necessary to retain all types of presets included in the setting preset group 50; it is acceptable to retain at least a portion of them. Furthermore, it is also acceptable not to retain the setting preset group 50.

On the other hand, the information processing device 200 has a browser 230 as a functional configuration. The browser 230 is realized by the processor 202 of the information processing device 200 executing a browser application 224. Graphs are displayed via the browser 230 provided by the execution of the browser application 224. Through the browser 230 of the information processing device 200, the user can select, edit, update, etc. the setting preset group 50 and the setting group 60, and can also view graphs generated by the graph generation module 74 of the control device 100.

Figure 11 is a schematic diagram showing a modified example of the functional configuration of the information processing system 1 according to this embodiment. Figure 11 shows another typical example of a configuration in which the control device 100 is responsible for the main processing, and the user checks graphs on the HMI 300.

The functional configuration example shown in Figure 11 is similar to the functional configuration example shown in Figure 10, except that the graph generation module 74 of the control device 100 transmits the generated graph to the HMI 300.

Figure 12 is a schematic diagram showing another modified example of the functional configuration of the information processing system 1 according to this embodiment. Figure 12 shows yet another typical example of a configuration in which the information processing device 200 is responsible for the main processing instead of the control device 100.

In the configuration example shown in FIG. 12, the information processing device 200 has functional modules similar to those of the control device 100 in the configuration examples shown in FIGS. 10 and 11. However, because the raw data 72 is collected by the control device 100, the information processing device 200 further includes a data acquisition module 71 for acquiring the raw data 72 acquired by the control device 100.

In the configuration example shown in Figure 12, the user operates the information processing device 200 to acquire raw data 72 from the control device 100 and check a graph on the information processing device 200 using the acquired raw data 72.

The configuration examples shown in Figures 10 to 12 are not limited to these, and the data acquisition module 70, sampling module 80, important data selection module 90, and graph generation module 74 can be implemented in any device.

<H. Example of important data settings>
Next, an example of setting important data for determining that the data is important data to be effectively displayed in a graph will be described.

(h1: multiple thresholds)
In the examples shown in FIGS. 5 and 6, data below a single threshold value 30 is considered to be abnormal data (important data), but multiple threshold values may be set for determining important data.

Figure 13 is a diagram illustrating an example of setting multiple thresholds included in the important data setting (important data judgment setting) according to this embodiment. Figure 13 shows an example in which two thresholds 31 and 32 are set. More specifically, data that exceeds threshold 31 and data that falls below threshold 32 are considered to be abnormal data (important data).

For example, in equipment where the air pressure of the monitored object must be maintained within an appropriate range, it is necessary to monitor both an abnormal increase (Abnormality A) and an abnormal decrease (Abnormality B) in air pressure. In such cases, a threshold value 31 corresponding to an abnormal increase and a threshold value 32 corresponding to an abnormal decrease may be set.

For example, for an abnormal increase (abnormality A), the maximum value in each bucket is selected as the important data to select, and for an abnormal decrease (abnormality B), the minimum value in each bucket is selected as the important data to select. Note that for buckets where important data does not exist, no important data to select is selected. In this way, by additionally selecting important data to select, it is possible to avoid missing abnormal increases or decreases in air pressure.

In the example shown in Figure 13, in addition to the sampling data for each bucket, three pieces of data (important data) labeled A, B, and C are selected as selected important data.

Figure 14 is a diagram illustrating another example of setting multiple thresholds included in the important data setting (important data judgment setting) according to this embodiment. Figure 14 shows an example in which two thresholds 33 and 34 are set. These thresholds are tiered; for example, data above threshold 33 (abnormality A) may be evaluated as requiring an inspection of the equipment and a detailed inspection of the product, while data below threshold 33 but above threshold 34 (abnormality B) may be evaluated as requiring only a detailed inspection of the product.

Figures 13 and 14 show an example of setting two thresholds, but more thresholds can be set. Furthermore, depending on the thresholds set, the algorithm for selecting important data (for example, the maximum/minimum/median values of important data in each bucket) can also be set arbitrarily.

In other words, any important data determination settings (algorithms and parameters) and important data selection settings (algorithms and parameters) can be used depending on the target raw data. By using important data settings that correspond to the target raw data, it is possible to more reliably select important data that should not be overlooked in the target raw data.

(h2: changing threshold)
The parameters (threshold values) included in the important data setting may be variable values rather than fixed values, i.e., the parameters included in the important data setting may not be fixed values but may be varied according to a function or any algorithm.

Figure 15 is a diagram illustrating an example of setting a variable threshold value included in the important data setting (important data judgment setting) according to this embodiment. Figure 15 shows an example in which a periodic function is set as the threshold value.

For example, if the air pressure of the vehicle being monitored is controlled so that it changes periodically, the criteria for abnormal data must also be linked to the periodic changes. Figure 15 shows an example in which the threshold value 35 is set using a periodic function.

In this way, the threshold may be set using any function, such as a periodic function.

In the example shown in Figure 15, in addition to the sampling data for each bucket, data marked with an A (important data) that exceeds the threshold value of 35 is selected as selected important data.

Figure 16 is a diagram illustrating another example of setting a variable threshold value included in the important data setting (important data determination setting) according to this embodiment. Figure 16 shows an example in which data that deviates from the overall trend is determined to be anomalous data (important data). More specifically, data that deviates significantly from the moving average of the time-series data is considered to be anomalous data (important data).

Figure 16 shows an example in which data that is a predetermined value (10 in this example) larger than the moving average 36 of the most recent five points is set as threshold value 37, and data that is a predetermined value (10 in this example) smaller than threshold value 38 is set as threshold value 38. Data that exceeds threshold value 37 is considered abnormal A, and data that is smaller than threshold value 38 is considered abnormal B. Then, selected important data to be used for graph display is selected from the abnormal data (important data).

In this way, the judgment criteria may be set as a value that is a specified range away from the moving average of the time series data.

Figure 17 is a diagram illustrating yet another example of important data settings (important data judgment settings) according to this embodiment. Figure 17 shows an example in which important data judgment settings, including thresholds, change depending on the value of the variable mode, which indicates the state.

More specifically, the variable mode takes on one of the values "0", "1", or "2". When the variable mode is "0", the important data setting is disabled; when the variable mode is "1", the threshold is set to "100" and any value less than the threshold is set as important data; and when the variable mode is "2", the threshold is set to "0" and any value greater than the threshold is set as important data.

In the example shown in Figure 17, in the section where the first variable mode is "2", data showing values greater than the threshold value "100" is considered to be important data. Data marked with the letter A shows a value greater than 100, so it is determined to be important data. On the other hand, data marked with the letter b is less than 100, so it is determined not to be important data.

In the next section, where the variable mode is "1," data that indicates a value smaller than the threshold value "0" is considered important data. Data marked with the symbol c is greater than 0, so it is not considered important data.

When the variable mode is again "0", the important data setting is disabled, and data marked with the symbols d and e is not considered to be important data.

I. Presetting Settings
For the above-mentioned sampling settings and important data settings, presets including basic setting examples may be prepared. A user may be able to select a preset and edit it as appropriate. Alternatively, a user may be able to create and save any preset.

Figure 18 is a schematic diagram showing an example of a user interface screen related to presets provided by the information processing system 1 according to this embodiment. Figure 18 shows an example of a preset for important data settings.

Referring to FIG. 18, the user interface screen 280 includes a pull-down menu 281 for selecting a preset and one or more display areas 282 showing the contents of the preset.

Each display area 282 includes a pull-down menu 283 for selecting the algorithm to be used in the important data judgment settings, input fields 284 and 285 for setting parameters associated with the selected algorithm, and a pull-down menu 286 for selecting the judgment criteria.

In addition, each display area 282 includes a pull-down menu 287 for selecting the selection criteria used in the important data selection settings.

The user can configure important data settings, including thresholds 37 and 38 as shown in FIG. 16, by making the desired settings on the user interface screen 280 shown in FIG. 18.

In this way, by preparing one or more presets, you can use similar settings when visualizing similar data, reducing the effort required for configuration.

<J. Changes in display accuracy over time>
Generally, the load of the display process depends on the downsampling cycle (bucket size). Therefore, for example, immediately after starting to display a graph, the bucket size may be increased to generate and display the graph, and the bucket size may be reduced as time passes while the graph is still displayed.

For example, immediately after the graph display begins, one point out of every 1000 points of data may be selected as sampling data, important data included in those 1000 points of data may be selected, and the data may then be merged to generate the graph. After some time has passed, the system may change so that one point out of every 100 points of data may be selected as sampling data, important data included in those 100 points of data may be selected, and the data may then be merged to generate the graph.

Figure 19 is a diagram illustrating the processing load depending on bucket size in the information processing system 1 according to this embodiment.

Figure 19 (A) shows an example where the bucket size is 5. If the bucket size is large, the number of buckets will be smaller, and therefore the number of data points included in the graph will be smaller, and the time required to display the graph will be shorter. On the other hand, Figure 19 (B) shows an example where the bucket size is 3. If the bucket size is small, the number of buckets will be larger, and therefore the number of data points included in the graph will be larger, and the time required to display the graph will be longer.

In this way, the control device 100 (or the information processing device 200) may reduce the predetermined number (bucket size) for selecting sampling data and selected important data over time.

By setting the bucket size relatively large immediately after the graph begins to be displayed, and then adjusting the bucket size to be smaller, the number of data points used to display the graph is relatively small immediately after the graph begins to be displayed. This allows the graph to be generated and displayed in a short amount of time, allowing the user to quickly grasp the overview of the graph. If the user does not switch to displaying another graph and time passes, a more detailed graph is displayed, allowing the user to grasp more detailed information contained in the graph.

<K. Target Data>
Next, raw data to be displayed in a graph according to this embodiment will be described.

(k1: type of target data)
In the above description, a typical example of downsampling is a process of resampling target raw data (time-series data) to reduce the sampling rate. However, the term "downsampling" in this specification is not limited to downsampling in the narrow sense, but also encompasses processes that target non-time-series data. In other words, the target raw data for graph display is not limited to time-series data, but includes any non-time-series data.

For example, downsampling may involve employing an algorithm that resamples data that constitutes a scatter plot plotted in two-dimensional space.

Figure 20 is a diagram illustrating another example of a graph display according to this embodiment. Figure 20 shows a scatter plot consisting of 50 points of data as an example of a graph. Such a scatter plot may, for example, show the inspection results of the positions of holes drilled in a plate-like workpiece. The samples indicate the (X, Y) coordinates in the coordinate system set for the workpiece.

In the graph shown in Figure 20, black circles (●) represent data selected based on arbitrary sampling settings, and white circles (○) represent data that was not selected (data not plotted).

In the graph shown in Figure 20, a reference frame 40 is set as the criteria for determining what is included in the important data setting, and data outside the reference frame 40 is considered important data. Based on the important data setting, some of this important data is selected as selected important data. More specifically, in the example shown in Figure 20, three pieces of data (important data) labeled A, B, and C, respectively, are selected as selected important data.

In the example shown in Figure 20, of the 50 raw data points of interest, the data used for the scatter plot has been thinned to 18 points.

As mentioned above, the raw data that is the subject of graph display in accordance with this embodiment is not limited to time-series data, but may also include non-time-series data.

Figure 21 is a diagram illustrating an example of important data selection settings according to this embodiment. Figure 21 shows an example of important data selection settings applied to a scatter plot plotted in two-dimensional space.

Referring to Figure 21, for example, for each bucket, data that exists outside the threshold range of a rectangle defined by four specified points can be considered important data. Furthermore, if multiple important data exist for each bucket, selected important data can be selected based on any algorithm.

As an example, an algorithm for selecting the important data may be one that maximizes the area of the polygon defined by the important data selected from each bucket.

In the graph shown in Figure 21, each bucket contains important data A1-A3, important data B1-B3, important data C1-C3, and important data D1-D3. That is, important data A1-A3 belong to one bucket, important data B1-B3 belong to another bucket, important data C1-B3 belong to yet another bucket, and important data D1-B3 belong to yet another bucket.

Assume that one of three important data items is selected for each bucket. In this example, a total of four important data items are selected as selected important data items. The combination is determined so that the area enclosed by the four selected important data items is maximized. As a result, important data items A1, B2, C3, and D4 are selected.

Note that the algorithm is not limited to maximizing area, and any selection algorithm can be used.

(k2: Add target data)
In this embodiment, not only is it possible to process raw data acquired in advance, but also data that is generated afterwards.

When processing pre-prepared data, the control device 100 (or information processing device 200) selects sampled data from the target raw data by downsampling, extracts important data contained in the target raw data, and selects at least a portion of the extracted important data as selected important data. Finally, the sampled data and selected important data are displayed in graphs.

At production sites, new data (such as measurement values from sensors) is generated sequentially as operations proceed. There is a need to be able to display this sequentially generated data in graphs.

To meet such needs, if there is new data to be added as target raw data, it may be possible to add it as a target for graph display. More specifically, in response to a user operation or the occurrence of new data to be added, the control device 100 (or information processing device 200) selects sampled data from the new data to be added by downsampling, extracts important data contained in the new data to be added, and selects at least a portion of the extracted important data as selected important data. Finally, the sampled data and selected important data for the new data to be added are displayed in graph form.

Figure 22 is a diagram illustrating an example of how a graph display according to this embodiment changes over time. With reference to Figure 22, for example, when a graph is displayed using already acquired data (raw data), after a predetermined time has passed, newly acquired data is added and the newly added data is added and displayed on the graph. In the example shown in Figure 22, two pieces of data have been added.

In this way, if the graph display is maintained while the production site is operating, newly generated data will be added to the graph sequentially. Users can obtain the information they need while checking the graph as data is added sequentially.

<L. Display mode＞
Next, a display mode of the graph display according to the present embodiment will be described.

(l1: Change in display mode)
As described above, the sampling data and selected important data are displayed in graph form, but the display manner, such as the graph format, may be changed depending on the type of data.

Figure 23 is a diagram illustrating an example of the display mode of a graph display according to this embodiment. Figure 23(A) shows an example in which anomalous data is included in time-series data whose values increase over time. For example, one sampling data item is selected for each bucket (e.g., the median value of each bucket is selected), and the anomalous data item is considered to be selected important data.

Figure 23(B) shows an example of a graphical representation of merged data obtained by merging the sampling data selected in Figure 23(A) with the selected important data.

The graph display shown in Figure 23 (B) makes it difficult to see that the target time-series data (raw data) has a characteristic in which values increase over time. In this way, if the sampled data and selected important data are displayed in the same graph format, the recognizability of the information may be reduced.

Therefore, sampling data and selected important data may be displayed so that they can be distinguished from each other.

Figure 24 is a diagram illustrating another example of the display mode of a graph according to this embodiment.

Figure 24 (A) shows an example in which the display modes (color, shape, etc.) of the figures representing sampled data and selected important data are different from each other. More specifically, sampled data is represented by a black circle (●), and selected important data is represented by a white triangle (△).

Figure 24(B) shows an example in which the graph formats showing the sampling data and the selected important data are different. More specifically, the sampling data is shown as a line graph, and the selected important data is shown as a scatter plot (plot only).

Figure 24 (C) shows an example in which objects (text, graphics, etc.) are attached according to the meaning of the data. More specifically, objects (alphabet) are attached only to selected important data, and no objects are attached to sampled data.

Note that objects may be attached only to sampling data, or objects with different meanings may be attached to different data.

In this way, the control device 100 (or the information processing device 200) may represent the sampling data in a graph in a first display format, and the selected important data in a graph in a second display format. By displaying the graph in a display format appropriate for the type of data, the meaning of the data displayed in the graph can be more clearly understood.

(l2: Display granularity of important data)
In the above description, when multiple important data items appear in one bucket, one important data item (selected important data item) is selected based on the important data setting. However, the important data setting may be changed depending on the situation. For example, all important data items may be regarded as selected important data items and a graph may be displayed.

Figure 25 is a diagram illustrating an example of changing the display granularity of important data according to this embodiment. Figure 25(A) shows an example of the change over time in the load on a motor. In the graph shown in Figure 25(A), black circles (●) represent data selected based on arbitrary sampling settings, and white circles (○) represent abnormal data where the load is greater than a predetermined threshold.

The graph shown in Figure 25(A) shows that abnormal data occurred with high frequency during a certain time period, and that the frequency of abnormal data occurrence subsequently decreased.

In contrast, Figure 25(B) shows an example of displaying important data (selected important data) selected from the important data based on the important data settings. This graph does not allow for the interpretation of changes over time in the frequency of abnormal data occurrence.

In this way, you can change the important data settings depending on the situation so that more important data is displayed in a graph. Displaying more important data in a graph will increase the number of data points displayed, and it may take more time to generate and display the graph, but displaying more important data in a graph will make it easier to grasp trends in the important data.

M. Generation of Representative Data
The above description illustrates downsampling, which selects one or more data from multiple data contained in a bucket, and important data setting, which selects one or more data from data considered to be important data. While the above describes a process of selecting a portion of the target raw data, it is also possible to generate new representative data that represents multiple data. In other words, the sampled data and selected important data may be data that is not included in the raw data.

(m1: average value of the ordinate and average value of the abscissa)
26 is a diagram for explaining an example of a process for generating representative data according to this embodiment, in which each bucket contains six pieces of data (raw data).

Representative data (equivalent to sampling data) is generated that represents the six pieces of data contained in each bucket. The value (ordinate) of the representative data is set to the average value of the six pieces of data contained in each bucket. The position of the representative data on the time axis (abscissa) is also set to the average value of the time of the six pieces of data contained in each bucket. In this way, representative data (sampling data) may be generated using the average values of the ordinate and abscissa for the multiple pieces of data contained in each bucket.

The abnormal data (important data) marked with the letter A is unique in Bucket 1, so its value is used as is. In contrast, in Bucket 2, there are two abnormal data (important data), so the representative data (selected important data) is generated using the average ordinate and abscissa values for the two abnormal data.

In this way, representative data (selected important data) may be generated using the average values of the ordinate and abscissa for the multiple important data contained in each bucket.

Figure 27 is a diagram for explaining the process of graph display using representative data generated by the process shown in Figure 26. The multiple representative data (sampling data) generated by the process shown in Figure 26 has coordinate values as shown in Figure 27(A). The multiple representative data (selected important data) generated by the process shown in Figure 26 has coordinate values as shown in Figure 27(B).

The control device 100 (or information processing device 200) generates a graph such as that shown in Figure 27(C) using the coordinate value groups shown in Figure 27(A) and Figure 27(B).

(m2: mean value of ordinate and center of abscissa)
28 is a diagram for explaining another example of processing for generating representative data according to this embodiment, in which each bucket contains four pieces of data (raw data).

Representative data (equivalent to sampling data) is generated that represents the four data points contained in each bucket. The value (ordinate) of the representative data is set to the average value of the four data points contained in each bucket. The position of the representative data on the time axis (abscissa) is set to the center of each bucket. In this way, representative data (sampling data) may be generated using the average value of the ordinates of the multiple data points contained in each bucket and the center of each bucket.

Abnormal data (important data) marked with A is unique in bucket 1, so its value is used as is. However, representative data (selected important data) may also be generated using the average ordinate value of multiple important data contained in each bucket and the center of each bucket.

Figure 29 is a diagram for explaining the process of graph display using representative data generated by the process shown in Figure 28. The multiple representative data (sampling data) generated by the process shown in Figure 28 have coordinate values as shown in Figure 29 (A). The multiple representative data (selected important data) generated by the process shown in Figure 29 have coordinate values as shown in Figure 29 (B).

The control device 100 (or information processing device 200) generates a graph such as that shown in Figure 29(C) using the coordinate value groups shown in Figure 29(A) and Figure 29(B).

In this way, by using the horizontal coordinate of the representative data (sampled data) as the center of each bucket, if all buckets have the same width, it is possible to generate a graph consisting of sampled data spaced equally apart horizontally, as shown in Figure 29(C). In other words, as shown in Figure 29(A), information on the horizontal coordinate is not required for sampled data, which reduces resource consumption such as memory. This type of specification is particularly beneficial for embedded devices, where resources are likely to be limited.

On the other hand, if we consider important data to be singular values, it is unlikely that important data will appear in all buckets. In other words, whether or not important data is included varies depending on the bucket, so even if the center of each bucket is set as the abscissa of the selected important data, the spacing between adjacent selected important data will not be constant. For this reason, it is preferable for selected important data to have information on both the ordinate and abscissa, as shown in Figure 29 (B).

However, sharing the same abscissa between the sampling data and the important selection data may improve visibility when the user views the graph, so the abscissa of the important selection data may be set to the center of the corresponding bucket.

N. Processing Procedure
Next, a description will be given of a processing procedure in the information processing system 1 according to the present embodiment. Note that the following description will be based on the configuration example shown in Fig. 10, but in other configuration examples, the entity that executes the processing can be changed as appropriate.

(n1: graph display)
First, the process relating to graph display will be described.

Figure 30 is a flowchart showing the processing steps for displaying a graph in the information processing system 1 according to this embodiment. The processing shown in Figure 30 is realized by the processor 102 of the control device 100 executing the application program 134, and by the processor 202 of the information processing device 200 executing the browser application 224.

The control device 100 determines whether or not it has received access from the browser 230 of the information processing device 200 (step S100). If it has not received access (NO in step S100), the processing from step S100 onwards is repeated.

When the user launches the browser 230 on the information processing device 200 and opens the graph display screen, the control device 100 accesses the information processing device 200.

If access has been received (YES in step S100), the information processing device 200 sends graph settings to the information processing device 200 (step S102). The graph settings include the size of the graph display area, the period and number of points of data to be displayed on one screen, etc. The browser 230 of the information processing device 200 generates a user interface screen based on the graph settings.

The control device 100 then determines whether a request for graph display has been received from the browser 230 of the information processing device 200 (step S104). If a request for graph display has not been received (NO in step S104), the process of step S104 is repeated.

If a request for graph display is received (YES in step S104), the control device 100 determines the bucket size based on the graph settings (step S106).

Then, the control device 100 selects sampling data from the target raw data based on the sampling settings (step S108). In other words, the control device 100 selects a smaller number of data (sampling data) from the multiple data to be displayed in a graph in accordance with a predetermined algorithm.

The control device 100 also extracts important data from the target raw data based on the important data settings, and selects selected important data from the extracted important data (step S110). In other words, the control device 100 selects data (selected important data) from multiple data sets in accordance with predetermined settings.

The order in which sampling data is selected and important data is selected may be any order.

The control device 100 then generates a graph including the sampling data and the selected importance data (step S112) and transmits it to the information processing device 200 (step S114). That is, the control device 100 generates a graph based on the sampling data and the selected importance data. The browser 230 of the information processing device 200 draws the graph from the control device 100 on the user interface screen. This process causes the graph to be displayed on the information processing device 200.

Next, the control device 100 determines whether there is data to be added to the raw data (step S116). If there is data to be added to the raw data (YES in step S116), the control device 100 sets the raw data containing the data to be added as a new target (step S118) and repeats the processing from step S108 onwards.

If there is no data to add to the raw data (NO in step S116), the control device 100 determines whether a predetermined time has passed since the previous graph generation (step S120). If the predetermined time has passed since the previous graph generation (YES in step S120), the control device 100 reduces the bucket size (step S122) and repeats the processing from step S108 onwards.

If a predetermined time has not elapsed since the previous graph generation (NO in step S120), the control device 100 determines whether the graph display in the browser 230 of the information processing device 200 is still active (step S124). If the graph display in the browser 230 is still active (YES in step S124), the process from step S116 onwards is repeated.

If the graph display on the browser 230 is not continuing (NO in step S124), the control device 100 ends the processing related to the graph display.

(n2: setting)
Next, the setting process will be described.

Figure 31 is a flowchart showing the processing steps related to setting in the information processing system 1 according to this embodiment. The processing shown in Figure 31 is realized by the processor 102 of the control device 100 executing the application program 134, and by the processor 202 of the information processing device 200 executing the browser application 224.

Referring to FIG. 31, the control device 100 determines whether a request to create or add a setting preset has been received from the browser 230 of the information processing device 200 (step S200). If a request to create or add a setting preset has been received (YES in step S200), the control device 100 executes a setting preset creation process (step S202).

If a request to create or add a setting preset has not been received (NO in step S200), the control device 100 determines whether a request to create or add a setting has been received from the browser 230 of the information processing device 200 (step S204). If a request to create or add a setting has been received (YES in step S204), the control device 100 determines whether the received request is a request to create or add a setting based on a preset (step S206).

If the received request is not a request to create or add settings based on a preset (NO in step S206), the control device 100 executes a process to create new settings (step S208). If the received request is a request to create or add settings based on a preset (YES in step S206), the control device 100 executes a process to create settings based on the setting preset (step S210).

If a request to create or add settings has not been received (NO in step S204), the setting process ends.

Figure 32 is a flowchart showing the processing steps for creating the setting preset shown in Figure 31. Referring to Figure 32, the control device 100 accepts a name to be assigned to the setting preset to be created from the user (step S220). Next, the control device 100 accepts a type of setting preset to be created from the user (step S222).

When a sampling setting preset is specified ("Sampling Setting Preset" in step S222), the control device 100 accepts the specification of the algorithm to be used for the sampling setting (step S224). Examples of algorithms include median, average, moving average, and Largest-Triangle-Three-Buckets. The control device 100 also accepts the specification of parameters to be used for the sampling setting (step S226). Examples of parameters include using the abscissa as the median value of the bucket. Finally, the control device 100 generates and stores a sampling setting preset including the specified algorithm and parameters (step S228). The process then returns.

When an important data setting preset is specified ("Important Data Setting Preset" in step S222), the control device 100 accepts the specification of the algorithm to be used for important data setting (step S230). Examples of algorithms include thresholds, moving averages, and functions. The control device 100 also accepts the specification of parameters to be used for important data setting (step S232). Examples of parameters include moving averages. The control device 100 also accepts the specification of selection criteria to be used for important data selection setting (step S234). Examples of parameters include selection of the maximum value of important data. Finally, the control device 100 generates and stores a sampling setting preset including the specified algorithm, parameters, and selection criteria (step S236). The process then returns.

When a graph setting preset is specified ("Graph setting preset" in step S222), the control device 100 accepts the specification of the graph type (step S238). Examples of graph types include line graphs and scatter plots. The control device 100 also accepts the specification of the columns to be used for each axis of the graph (step S240). For example, a column specification could be to specify the first column as the horizontal axis of a scatter plot and the second column as the vertical axis of the scatter plot. The control device 100 also accepts the specification of the display method and units for each axis (step S242). Finally, the control device 100 generates and stores a graph setting preset that includes the specified graph type, column specification, and display method and unit specifications (step S244). The process then returns.

Figure 33 is a flowchart showing the processing steps for creating new settings shown in Figure 31. Referring to Figure 33, the control device 100 accepts a designation from the user of the type of setting to be created (step S250).

When a sampling setting is specified ("Sampling setting" in step S250), the control device 100 accepts the specification of the algorithm to be used in the sampling setting (step S252). The control device 100 also accepts the specification of the parameters to be used in the sampling setting (step S254). Finally, the control device 100 generates and stores a sampling setting including the specified algorithm and parameters (step S256). The process then returns.

When important data setting is specified ("Important Data Setting" in step S250), the control device 100 accepts the specification of the algorithm to be used for the important data setting (step S258). The control device 100 also accepts the specification of parameters to be used for the important data setting (step S260). The control device 100 also accepts the specification of selection criteria to be used for the important data selection setting (step S262). Finally, the control device 100 generates and stores a sampling setting including the specified algorithm, parameters, and selection criteria (step S264). The process then returns.

When graph settings are specified ("Graph Settings" in step S250), the control device 100 accepts the specification of the graph type (step S266). The control device 100 also accepts the specification of the columns to be used for each axis of the graph (step S268). The control device 100 also accepts the specification of the display method and units for each axis (step S270). Finally, the control device 100 generates and stores graph settings including the specified graph type, column specification, display method and unit specification (step S272). The process then returns.

Figure 34 is a flowchart showing the processing steps for creating settings based on the setting presets shown in Figure 31. Referring to Figure 34, the control device 100 loads a specified setting preset from a group of setting presets prepared in advance (step S280). The control device 100 changes the loaded values (algorithms, parameters, etc.) in response to user operation (step S282). Finally, the control device 100 stores the settings including the changed values in response to user operation (step S284). The process then returns.

<O. Notes>
The present embodiment as described above includes the following technical idea.

[Configuration 1]
An information processing system (1),
a first data selection unit (22; 80) that selects a smaller number of first data (27; 82) from a plurality of data (21; 72) to be displayed in a graph in accordance with a predetermined first algorithm;
a second data selection unit (23; 90) for selecting second data (28; 92) from the plurality of data in accordance with a predetermined setting (64);
a graph generating unit (24; 74) that generates a graph based on the first data and the second data;
The information processing system, wherein the settings include a second algorithm and parameters for determining data to be displayed on the graph.

[Configuration 2]
2. The information processing system of claim 1, wherein the settings further include criteria for selecting a smaller number of data from the plurality of data determined to be displayed in the graph.

[Configuration 3]
the first data selection unit selects one of the first data for each predetermined number of data;
3. The information processing system according to configuration 1 or 2, wherein the second data selection unit selects one of the second data for each of the predetermined number of data.

[Configuration 4]
4. The information processing system according to claim 3, wherein the first data selection unit and the second data selection unit decrease the predetermined number for selecting the first data and the second data over time.

[Configuration 5]
The information processing system according to any one of configurations 1 to 4, wherein the graph generation unit represents the first data in the graph in a first display format and represents the second data in the graph in a second display format.

[Configuration 6]
6. The information processing system according to any one of configurations 1 to 5, wherein the setting parameters vary according to at least one of a function and a third algorithm.

[Configuration 7]
The information processing system according to any one of configurations 1 to 6, further comprising a storage unit configured to store at least one of a predefined first preset (52) available as the first algorithm and a predefined second preset (54) available as the setting.

[Configuration 8]
Further provided is an information processing device (200) that executes a browser application (224),
8. The information processing system according to any one of configurations 1 to 7, wherein the graph is displayed via a browser (230) provided by execution of the browser application.

[Configuration 9]
An information processing method, comprising:
a step (S108) of selecting a smaller number of first data (27; 82) from the plurality of data (21; 72) to be graphed in accordance with a predetermined first algorithm;
selecting (S110) second data (28; 92) from the plurality of data in accordance with a predetermined setting (64);
generating a graph based on the first data and the second data (S112, S114);
The information processing method, wherein the settings include a second algorithm and parameters for determining data to be displayed on the graph.

[Configuration 10]
An information processing program (134) for a computer (100; 200):
a step (S108) of selecting a smaller number of first data (27; 82) from the plurality of data (21; 72) to be graphed in accordance with a predetermined first algorithm;
selecting (S110) second data (28; 92) from the plurality of data in accordance with a predetermined setting (64);
and generating a graph based on the first data and the second data (S112, S114).
The information processing program, wherein the settings include a second algorithm and parameters for determining that the data should be displayed on the graph.

<P. Advantages>
According to the information processing system 1 of the present embodiment, data to be used for graph display is selected while maintaining the shape of the graph as much as possible, while also selecting data that is important (meaningful) to the user (important data). As a result, it is possible to reduce the resources required for graph display, and important data selected according to preset settings is also represented in the graph, reducing the possibility that the user will miss important data.

The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The scope of the present invention is indicated by the claims, not the above description, and is intended to include all modifications that are equivalent in meaning to and within the scope of the claims.

1 Information processing system, 2 Field bus, 4 Upper network, 10 Field device group, 12 Remote I/O device, 14 Relay group, 18 Servo driver, 20 Servo motor, 21, 72 Raw data, 22 First data selection unit, 23 Second data selection unit, 24 Graph generation unit, 25, 30, 31, 32, 33, 34, 35, 37, 38, 256 Threshold value, 26 Graph, 27 First data, 28 Second data, 36 Moving average, 40 Reference frame, 50 Setting preset group, 52 Sampling setting preset, 54 Important data setting preset, 56 Graph setting preset, 60 Setting group, 62 Sampling setting, 64 Important data setting, 66 Graph setting, 70, 71 Data acquisition module, 74 Graph generation module, 76 Display update module, 80 Sampling module, 82 Sampling data, 90 Important data selection module, 92 Selected important data, 100 control device, 102, 202 processor, 104 chipset, 106, 206 main memory device, 108, 208 secondary memory device, 110 upper network controller, 112, 212 USB controller, 114 memory card interface, 116 memory card, 120 field bus controller, 122 internal bus controller, 124 I/O unit, 131 system program, 132 user program, 133 application execution environment program, 134 application program, 200 information processing device, 204 optical drive, 205 recording medium, 214 network controller, 216 input unit, 218 display unit, 220 bus, 222 OS, 224 browser application, 230 browser, 240, 242, 280 user interface screen, 250, 251 graph display area, 252 line graph, 253, 254 Plot, 257 threshold range, 260 sampling setting area, 262, 272, 276, 278, 281, 283, 286, 287 pull-down, 264 slider, 270 important data setting area, 274, 275, 284, 285 input field, 282 display area, 300 HMI.

Claims (7)

An information processing system,
a first data selection unit that selects a smaller number of first data from a plurality of data to be graph-displayed in accordance with a predetermined first algorithm;
a second data selection unit that selects second data from the plurality of data in accordance with a predetermined setting;
a graph generation unit that generates a graph based on the first data and the second data,
The setting is:
a second algorithm and parameters for determining which data should be displayed on the graph ;
a criterion for selecting a smaller number of data from the plurality of data determined to be displayed on the graph ;
the first data selection unit selects one of the first data for each predetermined number of data;
the second data selection unit selects one of the second data for each of the predetermined number of data;
an information processing system, wherein the first data selection unit and the second data selection unit decrease the predetermined number for selecting the first data and the second data over time; The information processing system according to claim 1 , wherein the graph generating unit represents the first data in the graph in a first display format and represents the second data in the graph in a second display format. The information processing system according to claim 1 , wherein the parameters of the settings are varied according to at least one of a function and a third algorithm. The information processing system according to any one of claims 1 to 3, further comprising a storage unit that stores at least one of a predefined first preset that can be used as the first algorithm and a predefined second preset that can be used as the setting. further comprising an information processing device that executes a browser application;
5. The information processing system according to claim 1 , wherein the graph is displayed via a browser provided by executing the browser application. An information processing method, comprising:
selecting a smaller number of first data from the plurality of data to be graphed in accordance with a predetermined first algorithm;
selecting second data from the plurality of data in accordance with a predetermined setting;
generating a graph based on the first data and the second data;
The setting is:
a second algorithm and parameters for determining which data should be displayed on the graph ;
a criterion for selecting a smaller number of data from the plurality of data determined to be displayed on the graph ;
the step of selecting the first data includes the step of selecting one of the first data for every predetermined number of data;
the step of selecting the second data includes the step of selecting one of the second data for each of the predetermined number of data;
An information processing method, wherein in the step of selecting the first data and the step of selecting the second data, the predetermined number for selecting the first data and the second data decreases over time . An information processing program, which is installed on a computer,
selecting a smaller number of first data from the plurality of data to be graphed in accordance with a predetermined first algorithm;
selecting second data from the plurality of data in accordance with a predetermined setting;
generating a graph based on the first data and the second data;
The setting is:
a second algorithm and parameters for determining which data should be displayed on the graph ;
a criterion for selecting a smaller number of data from the plurality of data determined to be displayed on the graph ;
the step of selecting the first data includes the step of selecting one of the first data for every predetermined number of data;
the step of selecting the second data includes the step of selecting one of the second data for each of the predetermined number of data;
an information processing program, wherein in the step of selecting the first data and the step of selecting the second data, the predetermined number for selecting the first data and the second data decreases over time .