Data analysis device and program

Abstract

This invention provides a data analysis device and program that can understand the operating status of vehicles. [Solution] Server 20 acquires operational data from multiple vehicles 1, analyzes the operational data, and creates graphs G31 and G32 showing the proportions of idling, stopped, and driving states. Server 20 extracts the locations where the vehicle was idling. Server 20 displays the created graphs G31 and G32 side by side with a map M31 plotting the extracted locations.

Description

Technical Field

[0001] The present invention relates to a data analysis device and a program.

Background Art

[0002] Conventionally, an operation management system has been proposed that collects operation data from vehicles, analyzes the collected operation data, and manages the vehicles and drivers (Patent Document 1). In Patent Document 1, the driver is evaluated based on the number of rapid accelerations and rapid decelerations. However, there is a problem in that the operation status cannot be grasped only by the number of rapid accelerations and rapid decelerations, and it is difficult to grasp what problems existed in the operation.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention has been made in view of the above circumstances, and an object thereof is to provide a data analysis device and a program capable of grasping the operation status of a vehicle.

Means for Solving the Problems

[0005] In order to achieve the above object, the data analysis device according to the present invention is characterized by the following. An acquisition unit that acquires operation data from a plurality of vehicles, A graph creation unit that creates a graph showing the analysis result of the operation data, A point extraction unit that extracts points related to the analysis result, A display control unit that displays side by side the graph created by the graph creation unit and a map on which the points extracted by the point extraction unit are plotted. It is a data analysis device.

[0006] Furthermore, in order to achieve the aforementioned objectives, the program according to the present invention has the following features. Steps to acquire operational data from multiple vehicles, The steps include creating a graph showing the analysis results obtained by analyzing the aforementioned operational data, A step of extracting locations related to the aforementioned analysis results, The steps include displaying the created graph and the map on which the extracted points are plotted side by side, It is a program that causes a computer to execute something. [Effects of the Invention]

[0007] The data analysis device and program according to the present invention have the effect of being able to grasp the operating status of vehicles.

[0008] The present invention has been briefly described above. Furthermore, the details of the present invention will be further clarified by referring to the attached drawings and reading through the embodiments for carrying out the invention described below (hereinafter referred to as "embodiments"). [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 is a system configuration diagram showing an example of a data analysis system incorporating the data analysis device of the present invention. [Figure 2] Figure 2 is a diagram showing an example configuration of the digital tachograph shown in Figure 1. [Figure 3] Figure 3 is a configuration diagram showing an example of the server configuration shown in Figure 1. [Figure 4] Figure 4 is a flowchart showing the processing procedure of the server shown in Figure 1. [Figure 5] Figure 5 shows an example of how analysis data for rapid acceleration and deceleration is displayed. [Figure 6]Figure 6 shows an example of how engine idling analysis data is displayed. [Figure 7] Figure 7 shows an example of how to display the analysis data for driving efficiency. [Figure 8] Figure 8 shows an example of how to display analysis data for driving efficiency. [Modes for carrying out the invention]

[0010] Specific embodiments of the present invention will be described below with reference to the figures.

[0011] As shown in Figure 1, the data analysis system 100 according to this embodiment is a system that collects operational data from vehicle 1, analyzes the operational data, and displays the results. The data analysis system 100 comprises a digital tachograph 10, a server 20, and an administrator PC (personal computer) 40. The digital tachograph 10 is an example of an in-vehicle device mounted on vehicle 1. The server 20 is an example of a data analysis device that can communicate with the digital tachograph 10. The administrator PC 40 is an example of a communication terminal that can communicate with the server 20.

[0012] The digital tachograph 10 is mounted on a vehicle 1, such as a truck, and collects operational data from the vehicle 1. Specifically, the digital tachograph 10 is an operation recorder that records the vehicle 1's speed, driving time, distance traveled, etc. The digital tachograph 10 can be wirelessly connected to a network N, such as the internet, via a base station BS, for example, by wireless communication. The digital tachograph 10 is sometimes simply called "DigiTach 10".

[0013] Server 20 is a computer device that can communicate with the digital tachograph 10 via the network N and exchanges operational data of vehicle 1 with the digital tachograph 10. Server 20 is installed, for example, by a service provider that provides operational data analysis services, but it may also be installed by the operator of vehicle 1 (for example, a transportation company).

[0014] The management PC 40 is a communication terminal used by vehicle managers or the like of the operator who operates the vehicle 1, and can communicate with at least one of the digital tachograph 10 and the server 20 via the network N, and in this embodiment, it can communicate with both. The communication terminal is not limited to a fixed installation type such as a business-use PC, and may be a portable device such as a tablet, a smartphone, a mobile phone, or the like.

[0015] FIG. 2 is a block diagram showing a configuration example of the digital tachograph 10 according to the embodiment. The digital tachograph 10 includes a control unit 11, a time information acquisition unit 12, a position information acquisition unit 13, a speed information acquisition unit 14, an engine information acquisition unit 15, and a communication unit 16.

[0016] The control unit 11 is an arithmetic processing device (computer) that performs the main control of the digital tachograph 10. The control unit 11 reads out an in-vehicle device program stored in a memory (not shown) and causes each part of the digital tachograph 10 to execute a predetermined process.

[0017] The time information acquisition unit 12 acquires current time information from a clock built in the digital tachograph 10 and / or another server via the network N, GPS satellites S, or the like. The position information acquisition unit 13 acquires position information (including latitude and longitude) of the vehicle 1 from the GPS satellites S. The speed information acquisition unit 14 acquires the traveling speed of the vehicle 1 from a speedometer or the like of the vehicle 1. The engine information acquisition unit 15 acquires engine information including engine on / off information corresponding to an ignition signal representing the presence or absence of ignition of the engine of the vehicle 1. Specifically, the ignition signal is either on or off, and for example, it is a voltage signal of a detection circuit that detects the voltage of an ignition coil that ignites the engine.

[0018] The time information acquisition unit 12, the location information acquisition unit 13, the speed information acquisition unit 14, and the engine information acquisition unit 15 acquire time information, location information, speed information, and engine information at arbitrary time intervals. The time intervals for acquiring various data can be set arbitrarily, and the shorter the time interval and the higher the acquisition frequency, the more precise the analysis becomes possible. On the other hand, increasing the data acquisition frequency increases the cost in terms of data processing and storage. Therefore, it is desirable to set the data acquisition frequency according to the situation.

[0019] The communication unit 16 functions as a transmitter that transmits operational data of vehicle 1, including location information, driving speed, engine information, etc., linked to the vehicle's identification information (ID) and time information, to the server 20 via the network N at any arbitrary time. The communication unit 16 also functions as a receiver that receives information from the server 20, administrator PC 40, etc.

[0020] Figure 3 is a block diagram showing an example configuration of a server 20 according to an embodiment. The server 20 comprises a control unit 21, a communication unit 22, and a database (DB) 23.

[0021] The control unit 21 is the arithmetic processing unit (computer) that is primarily responsible for controlling the server 20. The control unit 21 reads the server program stored in memory (not shown) and causes each part of the server 20 to execute predetermined processes.

[0022] The communication unit 22 functions as a receiving unit that receives vehicle operation data from the digital tachograph 10 via the network N. The communication unit 22 also functions as a transmitting unit that sends information to the digital tachograph 10, the administrator PC 40, etc. The communication unit 22 receives vehicle operation data transmitted from the digital tachograph 10 at any arbitrary time.

[0023] DB23 stores operational data acquired from the digital tachograph 10, as well as the results of the analysis of that operational data.

[0024] Next, the operation of the data analysis system 100 with the configuration described above will be explained below with reference to the flowchart shown in Figure 4. The digital tachograph 10 periodically transmits the collected operation data to the server 20. The server 20 functions as an acquisition unit and stores the received operation data in the DB 23.

[0025] When the administrator accesses the webpage for the data analysis system 100 using the administrator PC 40 and logs in, the server 20 can supply the analysis data. In this embodiment, the server 20 supplies three types of analysis data: rapid acceleration / deceleration, engine idling, and operational efficiency.

[0026] When the administrator uses the administrator PC 40 to select one of the following: rapid acceleration / deceleration, engine idling, or operational efficiency, and commands the provision of analysis data, an analysis command is sent from the administrator PC 40 to the server 20. The analysis command includes the type of analysis data selected by the administrator (rapid acceleration / deceleration, engine idling, or operational efficiency).

[0027] When server 20 receives an analysis command (S1), it functions as a graph creation unit, performs analysis according to the type of analysis data included in the analysis command, and creates a graph showing the analysis results (S2). Next, server 20 functions as a point extraction unit and extracts points related to the analysis results (S3). After that, server 20 functions as a display control unit and sends the graph created in S1 and the points extracted in S3 to administrator PC 40 (S4), and returns to S1. Administrator PC 40 displays the graph received from server 20 and a map plotting the received points side by side.

[0028] Next, we will explain the details of the analysis of sudden acceleration / deceleration, engine idling, and operational efficiency mentioned above. First, we will explain sudden acceleration / deceleration. When sudden acceleration / deceleration is selected, the server 20 extracts operational data for a predetermined period from DB 23 in S2. The predetermined period may be specified by the operator, or it may be automatically set to a period, for example, about one week prior to the day the analysis command was sent. In this embodiment, we will explain an example of extracting operational data for all vehicles 1.

[0029] Server 20 analyzes the extracted operational data and creates a graph G1 (see Figure 5) that shows the number of occurrences (frequency) of each of the following driving conditions within a predetermined period: sudden acceleration, sudden deceleration, strong acceleration, and strong deceleration.

[0030] Rapid acceleration is acceleration where the speed change is, for example, between 11 km / h and 15 km / h. Rapid deceleration is deceleration where the speed change is, for example, between 11 km / h and 15 km / h. Strong acceleration is a more gradual acceleration than rapid acceleration, where the speed change is, for example, between 7 km / h and 10 km / h. Strong deceleration is a more gradual deceleration than rapid deceleration, where the speed change is, for example, between 7 km / h and 10 km / h.

[0031] In the example shown in Figure 5, graph G1 is created showing the number of times sudden acceleration, sudden deceleration, strong acceleration, and strong deceleration occurred each day during the period from June 5, 2024 to June 11, 2024 (a predetermined period).

[0032] Server 20 extracts locations where sudden acceleration, sudden deceleration, strong acceleration, or strong deceleration occurred based on the operational data in S3. As a result, the administrator PC 40 displays graph G1 and map M1, which plots the locations where sudden acceleration, sudden deceleration, strong acceleration, or strong deceleration occurred, side by side, as shown in Figure 5.

[0033] By comparing the graph G1 and map M1 mentioned above, it is immediately clear when and where sudden acceleration and deceleration occurred, allowing for a clear understanding of the operational situation.

[0034] Next, we will explain engine idling. When engine idling is selected, in S2, the server 20 extracts operational data for all vehicles 1 from DB23 for a predetermined period. Based on the extracted operational data, the server 20 creates a graph G2 (see Figure 6) that shows the time the vehicle is in an idling state (operating state) (idling time; occurrence time) within the predetermined period.

[0035] In the example shown in Figure 6, graph G2 is created that shows the time progression of idling time during the period from June 5, 2024 to June 11, 2024 (a predetermined period).

[0036] Server 20 extracts locations where idling occurred based on the operational data extracted in S3. As a result, the administrator PC 40 displays graph G2 and map M2, which plots the locations where idling occurred, side by side, as shown in Figure 6. Map M2 may be plotted with different idling durations: long idling times in red, medium idling times in yellow, and short idling times in blue, to allow for easy identification of idling durations.

[0037] By comparing the graph G2 and map M2 mentioned above, it is immediately clear when and where idling occurred, allowing for a clear understanding of the operational status. For example, in the example shown in Figure 6, it can be seen that idling occurred around the loading and unloading area. This indicates that the loading area was congested, and vehicle 1 was waiting in the vicinity. In other words, it is clear that this is an operational issue rather than a driver issue, allowing for an accurate understanding of the operational status.

[0038] Next, we will explain driving efficiency. When driving efficiency is selected, server 20 extracts the driving data for all vehicles 1 for the third operating period from DB23 in S2, with the first point A as the departure point and the second point B as the destination. Server 20 analyzes the extracted driving data and creates graphs G31 and G32 showing the time spent in driving state, stopped state, and idling state (driving state) and the percentage of each within the third operating period (see Figure 7). The first point A and second point B can be selected by the operator.

[0039] To explain in more detail, server 20 divides the extracted operational data into operational data for the first operational period within a predetermined range R1 including the departure point A, and operational data for the second operational period from the predetermined range R1 to the destination point B. Server 20 analyzes the operational data for the first operational period to create graph G31 (first graph), and analyzes the operational data for the second operational period to create graph G32 (second graph).

[0040] Server 20 extracts locations where idling occurred based on the operation data for the third operating period in S3. As a result, the administrator PC 40 displays graphs G31 and G32 side by side, as shown in Figure 7. The administrator PC 40 also displays graphs G31 and G32 and map M3, which plots the locations where idling occurred, side by side. Because map M3 in Figure 7 has a large scale, multiple plots are combined into a single plot, and the combined number is displayed.

[0041] By comparing the graphs G31 and G32 mentioned above with map M3, it is immediately clear what proportion of idling, stopping, and driving conditions occurred, and where idling occurred, allowing for an understanding of the operating conditions. In the example shown in Figure 7, map M3 plotted only the locations where idling occurred, but it is not limited to this; it may also plot only the locations where stopping occurred, or plot locations where stopping and idling occurred in a way that allows for identification.

[0042] Furthermore, by comparing graphs G31 and G32, it is possible to understand the operational status around the starting point A and the operational status while traveling away from the starting point A.

[0043] It should be noted that the present invention is not limited to the embodiments described above, and various modifications can be adopted within the scope of the present invention. For example, the present invention is not limited to the embodiments described above, and can be modified, improved, etc. as appropriate. Furthermore, the material, shape, dimensions, number, placement, etc. of each component in the embodiments described above are arbitrary and not limited as long as they can achieve the present invention.

[0044] In the embodiment described above, as shown in Figure 7, the server 20 extracted operational data for a third operational period with the first point A as the departure point and the second point B as the destination, and displayed graphs G31 and G32 (third graph) and map M31 (first map) obtained by analyzing this data. However, it is not limited to this. As shown in Figure 8, the server 20 may also extract operational data for a fourth operational period with the second point B as the departure point and the first point A as the destination, and display graphs G33 and G34 (fourth graph) and map M32 (second map) obtained by analyzing this data side by side.

[0045] Alternatively, the operational data for the first operational period within a predetermined range R2 including the second point B may be divided into operational data for the second operational period from the predetermined range to the destination, the first point A. The operational data for the first and second operational periods may then be analyzed to create graphs G33 (first graph) and G34 (second graph), respectively.

[0046] This makes it possible to more accurately grasp the operating status of vehicle 1, which makes repeated round trips between point A and point B.

[0047] In the embodiment described above, the analysis data for sudden acceleration / deceleration and engine idling was displayed by side: graph G1 (third graph) which analyzed the operation data for the first predetermined period from June 5, 2024 to June 11, 2024, and map M1 (first map) which plotted the locations extracted during June 5, 2024 to June 11, 2024. However, this is not the only way. Server 20 may also create an unillustrated graph (fourth graph) which analyzes the operation data for a different period, for example from June 5, 2023 to June 11, 2023 (second predetermined period), and display this graph side by side with the map (second map) which plotted the locations extracted during June 5, 2023 to June 11, 2023.

[0048] By comparing graphs and maps from different periods in this way, it is possible to understand the vehicle operation status more accurately.

[0049] According to the embodiment described above, operational data for all vehicles was extracted, but this is not the only method. Operational data for individual vehicles may also be extracted. In this case, the operational status of each individual vehicle can be understood. Alternatively, operational data for vehicles that have been pre-grouped may be extracted. The grouping can be based on vehicle type, work content, etc. In this case, the operational status of each group can be understood.

[0050] Herein, the features of the embodiments of the data analysis device and program according to the present invention described above are briefly summarized and listed below in [1] to

[10] .

[0051] [1] An acquisition unit (21) that acquires operational data from multiple vehicles (1), A graph creation unit (21) creates a graph showing the analysis results obtained by analyzing the aforementioned operation data, A location extraction unit (21) extracts locations related to the aforementioned analysis results, The system includes a display control unit (21) that displays side by side the graphs (G1, G2, G31~G34) created by the graph creation unit (21) and the maps (M1, M2, M31, M32) on which the points extracted by the point extraction unit (21) are plotted. Data analysis device (20).

[0052] According to the data analysis device (20) with the configuration described in [1] above, the operating status of the vehicles can be grasped.

[0053] [2] In the data analysis device (20) described in [1], The graph creation unit (21) analyzes the operation data and creates graphs (G31, G32) that show the time spent in a predetermined number of different driving states within a predetermined period, and the proportion of each state. The location extraction unit (21) extracts locations that were in at least one of a plurality of operating states during the predetermined period. Data analysis device (20).

[0054] According to the data analysis device (20) with the configuration described in [2] above, by comparing the graphs (G31, G32) and the map (M31), it is possible to see at a glance what proportion of multiple types of driving conditions (for example, idling, stopped, and driving) occurred, and where each type of driving condition occurred, thereby understanding the operational status.

[0055] [3] In the data analysis device (20) described in [1], The graph creation unit (21) analyzes the operation data and creates graphs (G1, G2) showing the frequency or duration of occurrence of a predetermined driving condition within a predetermined period. The location extraction unit (21) extracts locations where a predetermined operating state occurs during the predetermined period. Data analysis device (20).

[0056] According to the data analysis device (20) with the configuration described in [3] above, by comparing the graphs (G1, G2) and the maps (M1, M2), it is immediately clear when and where the specified operating conditions occurred, and the operating status can be grasped.

[0057] [4] In the data analysis device (20) described in [2], The aforementioned multiple operating states are: stopped, running, and idling. Data analysis device (20).

[0058] According to the data analysis device (20) with the configuration described in [4] above, by comparing the graphs (G31, G32) and the map (M31), it is immediately clear what proportion of the vehicle was stopped, running, or idling, and where at least one of these conditions occurred, allowing for an understanding of the operating conditions.

[0059] [5] In the data analysis device (20) described in [3], The predetermined driving conditions are one of the following: rapid acceleration, rapid deceleration, idling, or exceeding the speed limit. Data analysis device (20).

[0060] According to the data analysis device (20) with the configuration described in [5] above, by comparing the graphs (G1, G2) and the maps (M1, M2), it is immediately clear when and where one of the following occurred: sudden acceleration, sudden deceleration, idling, or speeding, allowing for an understanding of the operating conditions.

[0061] [6] In the data analysis device (20) described in any one of items [1] to [5], The graph creation unit (21) creates a first graph (G31) which analyzes the operation data for the first operation period within a predetermined range including the departure point, and a second graph (G32) which analyzes the operation data for the second operation period from the predetermined range including the departure point to the destination, as the graphs. The display control unit displays the first graph (G31) and the second graph (G32) side by side. Data analysis device (20).

[0062] According to the data analysis device (20) with the configuration described in [6] above, by comparing the first and second graphs (G31, G32), it is possible to understand the operational status around the departure point and the operational status while traveling away from the departure point.

[0063] [7] In the data analysis device (20) described in any one of items [1] to [5], The graph creation unit (21) creates a third graph (G31, G32) which analyzes the operation data for a third operation period with the first point (A) as the departure point and the second point (B) as the destination, and a fourth graph (G33, G34) which analyzes the operation data for a fourth operation period with the second point (B) as the departure point and the first point (A) as the destination, as the graphs. The display control unit (21) displays the third graph (G31, G32), the fourth graph (G33, G34), the first map (M31) plotting the locations extracted during the third operating period, and the second map (M32) plotting the locations extracted during the fourth operating period side by side. Data analysis device (20).

[0064] According to the data analysis device (20) with the configuration described in [7] above, the operating status of a vehicle (1) that repeatedly travels back and forth between a first location (A) and a second location (B) can be grasped more accurately.

[0065] [8] In the data analysis device (20) described in any one of items [1] to [5], The graph creation unit (21) creates a fifth graph, which analyzes the operation data for a first predetermined period, and a sixth graph, which analyzes the operation data for a second predetermined period different from the first predetermined period, as the graphs. The display control unit (21) displays the fifth graph, the sixth graph, a third map plotting the points extracted during the first predetermined period, and a fourth map plotting the points extracted during the second predetermined period side by side. Data analysis device (20).

[0066] According to the data analysis device (20) with the configuration described in [8] above, the operating status of the vehicle (1) can be grasped more accurately by comparing graphs and maps for different first and second predetermined periods.

[0067] [9] In the data analysis device (20) described in any one of items [1] to [5], Multiple vehicles (1) are pre-grouped, The graph creation unit (21) analyzes the operation data obtained from the vehicle (1) of the designated group to create the graph. The location extraction unit (21) extracts locations based on the operation data obtained from the vehicle (1) of the designated group. Data analysis device (20).

[0068] According to the data analysis device (20) with the configuration described in [9] above, the operational status of each group can be grasped.

[0069]

[10] Steps include acquiring operational data from multiple vehicles (1), The steps include creating a graph showing the analysis results obtained by analyzing the aforementioned operational data, A step of extracting locations related to the aforementioned analysis results, The steps include displaying the created graph and the map on which the extracted points are plotted side by side, A program that causes a computer to execute something.

[0070] According to the configuration described in

[10] above, the operating status of the vehicles can be monitored. [Explanation of Symbols]

[0071] 1 vehicle 20 Servers (Data Analysis Devices) 21 Control Unit (Acquisition Unit, Graph Creation Unit, Location Extraction Unit) A 1st point B 2nd point G1 Graph G2 Graph G31 Graphs (Graph 1, Graph 3) G32 Graphs (Graph 2, Graph 3) G33 Graphs (Graph 1, Graph 4) G34 Graphs (Graph 2, Graph 4) M1 Map M2 Map M31 Map (Map 1) M32 Map (Second Map)

Claims

1. An acquisition unit that acquires operational data from multiple vehicles, A graph creation unit that creates a graph showing the analysis results obtained by analyzing the aforementioned operational data, A location extraction unit that extracts locations related to the aforementioned analysis results, The system includes a display control unit that displays the graph created by the graph creation unit and a map plotting the points extracted by the point extraction unit side by side. Data analysis device.

2. In the data analysis apparatus according to claim 1, The graph generation unit analyzes the operation data and generates a graph showing the time spent in a predetermined number of different driving states within a specified period, and the proportion of each state. The location extraction unit extracts locations that were in at least one of a plurality of operating states during the predetermined period. Data analysis device.

3. In the data analysis apparatus according to claim 1, The graph generation unit analyzes the operation data and creates a graph showing the frequency or duration of occurrence of a predetermined driving condition within a predetermined period. The location extraction unit extracts locations where a predetermined operating state occurs during the predetermined period. Data analysis device.

4. In the data analysis device according to claim 2, The aforementioned multiple operating states are: stopped, running, and idling. Data analysis device.

5. In the data analysis device according to claim 3, The predetermined driving conditions are one of the following: rapid acceleration, rapid deceleration, idling, or exceeding the speed limit. Data analysis device.

6. In the data analysis apparatus according to any one of claims 1 to 5, The graph creation unit creates a first graph which analyzes the operation data for a first operation period within a predetermined range including the departure point, and a second graph which analyzes the operation data for a second operation period from the predetermined range including the departure point to the destination, as the graphs. The display control unit displays the first graph and the second graph side by side. Data analysis device.

7. In the data analysis apparatus according to any one of claims 1 to 5, The graph creation unit creates a third graph, which analyzes the operation data for a third operation period with the first point as the departure point and the second point as the destination, and a fourth graph, which analyzes the operation data for a fourth operation period with the second point as the departure point and the first point as the destination, as the graphs. The display control unit displays the third graph, the fourth graph, a first map plotting the locations extracted during the third operating period, and a second map plotting the locations extracted during the fourth operating period side by side. Data analysis device.

8. In the data analysis apparatus according to any one of claims 1 to 5, The graph creation unit creates a fifth graph, which analyzes the operation data for a first predetermined period, and a sixth graph, which analyzes the operation data for a second predetermined period different from the first predetermined period, as the graphs. The display control unit displays the fifth graph, the sixth graph, a third map plotting the points extracted during the first predetermined period, and a fourth map plotting the points extracted during the second predetermined period side by side. Data analysis device.

9. In the data analysis apparatus according to any one of claims 1 to 5, Multiple of the aforementioned vehicles were pre-grouped, The graph creation unit analyzes the operation data acquired from the vehicles of the designated group to create the graph. The location extraction unit extracts locations based on the operation data obtained from the vehicles of the designated group. Data analysis device.

10. Steps to acquire operational data from multiple vehicles, The steps include creating a graph showing the analysis results obtained by analyzing the aforementioned operational data, A step of extracting locations related to the aforementioned analysis results, The steps include displaying the created graph and the map on which the extracted points are plotted side by side, A program that causes a computer to execute something.

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