Operation management system and information processing device

Abstract

To restrain increase in running cost associated with updating software to be used to control the operation of a fuel cell module.SOLUTION: A plurality of fuel cell modules FCM transmit, to an information processing device 2, identification information IDfc for identifying a fuel cell module FCM, identification information IDsp for identifying the configuration content of the fuel cell module FCM, identification information IDsm for identifying the version of software to be used to control the operation of the fuel cell module FCM, and operation data indicating the operation status of the fuel cell module FCM while associating them with one another. The information processing device 2 analyzes the operation data corresponding to the identification information IDfc based on an analysis pattern corresponding to the identification information IDsp and a sampling period corresponding to the identification information IDsm.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing device that analyzes operation data that indicates the operating state of a fuel cell module, and an operation management system that includes the information processing device. [Background technology]

[0002] In one operation management system, a plurality of fuel cell modules each acquires operation data at an arbitrary sampling period and transmits the operation data to an information processing device.

[0003] Furthermore, in the above-mentioned operation management system, when the analysis content is related to time, it is necessary to analyze the operation data taking into consideration the sampling period of the operation data.

[0004] Furthermore, as product development of fuel cell modules progresses and software used to control the operation of the fuel cell modules is updated, the sampling period of the fuel cell modules may change, and the sampling periods of each fuel cell module may differ from each other.

[0005] However, in the above-mentioned operation management system, when the analysis content is time-related and only one sampling period can be taken into consideration, even if the software is updated, the sampling period of each fuel cell module needs to be adjusted to the minimum sampling period, and there is a concern that an increase in the amount of data sent from the fuel cell module to the information processing device will result in an increase in running costs, such as communication charges and costs for expanding the memory area of ​​the information processing device. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Patent Publication No. 2022-95272 Summary of the Invention [Problem to be solved by the invention]

[0007] An object according to one aspect of the present invention is to provide an operation management system and an information processing device that are capable of suppressing increases in running costs associated with updates to software used to control the operation of a fuel cell module. [Means for solving the problem]

[0008] One embodiment of the present invention is an operation management system comprising a plurality of fuel cell modules and an information processing device, wherein the plurality of fuel cell modules transmit to the information processing device first identification information for identifying the fuel cell module, second identification information for identifying the configuration of the fuel cell module, third identification information for identifying the version of software used to control the operation of the fuel cell module, and operation data indicating the operating status of the fuel cell module in correspondence with each other, and the information processing device analyzes the operation data corresponding to the first identification information based on an analysis pattern corresponding to the second identification information and a sampling period corresponding to the third identification information.

[0009] As a result, even if the sampling period of operational data differs among multiple fuel cell modules according to the software version, the operational data can be analyzed based on the sampling period of each fuel cell module, and there is no need to adjust the sampling period of each fuel cell module to the minimum sampling period. Therefore, since the sampling period of at least one of the fuel cell modules can be made longer than the other sampling periods, the amount of data transmitted from the fuel cell module to the information processing device can be reduced, and the increase in running costs associated with software updates can be suppressed.

[0010] The information processing device may be configured to add, delete, or update a sampling period corresponding to the third identification information in response to a user operation.

[0011] This makes it possible to easily add, delete, or update the sampling period corresponding to the third identification information through a user operation.

[0012] The plurality of fuel cell modules may be configured to acquire the operational data at the sampling period.

[0013] Furthermore, an information processing device according to one embodiment of the present invention includes a communication unit that receives first identification information for identifying the fuel cell module, second identification information for identifying the configuration of the fuel cell module, third identification information for identifying the version of software used to control the operation of the fuel cell module, and operation data indicating the operating status of the fuel cell module, all of which are transmitted from a plurality of fuel cell modules, and an analysis unit that analyzes the operation data corresponding to the first identification information based on an analysis pattern corresponding to the second identification information and a sampling period corresponding to the third identification information.

[0014] As a result, even if the sampling period for operational data differs between multiple fuel cell modules depending on the software version, the operational data can be analyzed according to the respective sampling period of each fuel cell module, so there is no need to adjust the respective sampling period of each fuel cell module to the minimum sampling period, which reduces the amount of data sent from the fuel cell module to the information processing device and suppresses the increase in running costs associated with software updates. Effect of the Invention

[0015] According to the present invention, it is possible to suppress an increase in running costs that would otherwise be caused by updating software used to control the operation of a fuel cell module. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 illustrates an example of an operation management system according to an embodiment. [Diagram 2] 13 is a flowchart showing an example of an operation of an analysis unit. [Diagram 3] FIG. 4 is a diagram illustrating an example of operational data extracted from a storage unit. [Figure 4] 13 is a diagram showing an example of a correspondence relationship between analysis contents, analysis patterns, identification information IDsp, identification information IDsm, and a sampling period. FIG. [Diagram 5] FIG. 13 is a diagram illustrating an example of an analysis result. [Figure 6] FIG. 13 is a diagram illustrating an example of an analysis result. [Figure 7] FIG. 10 illustrates another example of the operation management system of the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, the embodiments will be described in detail with reference to the drawings.

[0018] FIG. 1 is a diagram illustrating an example of an operation management system according to an embodiment.

[0019] The operation management system 1 shown in Fig. 1 includes multiple fuel cell modules FCM (FCM1, FCM2) and an information processing device 2. Each of the multiple fuel cell modules FCM transmits and receives data to and from the information processing device 2 via a base station B and a network N such as the Internet. The number of fuel cell modules FCM shown in Fig. 1 is "2", but is not particularly limited.

[0020] The fuel cell modules FCM1 and FCM2 are each mounted on a vehicle Ve, such as a forklift, a towing tractor, or an automatic guided vehicle (AGV), and supply power to a load Lo mounted on the vehicle Ve. The load Lo shown in Fig. 1 is, for example, an inverter that drives a cargo handling device or a traveling motor.

[0021] Each of the fuel cell modules FCM1 and FCM2 includes a fuel cell stack FCS, an auxiliary device Au, a memory unit Sfc, and a communication unit Cfc.

[0022] The fuel cell stack FCS is composed of multiple fuel cell units connected in series with each other, and generates electricity through an electrochemical reaction between the hydrogen contained in the fuel gas (hydrogen gas) and the oxygen contained in the oxidant gas (air).

[0023] The auxiliary equipment Au is parts and equipment necessary for generating electricity in the fuel cell stack FCS, such as an injector for adjusting the amount of fuel gas supplied to the fuel cell stack FCS and an air compressor for supplying oxidant gas to the fuel cell stack FCS, and their operation is controlled by the control unit Cnt.

[0024] The storage unit Sfc is configured with a memory such as a RAM (Random Access Memory) or a ROM (Read Only Memory).

[0025] The communication unit Cfc is configured with an existing communication terminal such as a mobile phone or a smartphone compatible with a communication standard such as a fourth-generation mobile communication standard or a fifth-generation mobile communication standard, and transmits data received from the control unit Cnt to a base station B near the fuel cell module FCM by wireless communication. The base station B transmits the data transmitted from the communication unit Cfc to the information processing device 2 via the network N. The communication unit Cfc does not have to be configured with an existing communication terminal, and may be configured with a communication terminal newly developed for the operation management system 1. The communication unit Cfc may be included in the control unit Cnt. The communication protocol between the communication unit Cfc and the control unit Cnt is assumed to be, but is not limited to, a controller area network (CAN) or universal asynchronous receiver / transmitter (UART). The communication protocol between the communication unit Cfc and the information processing device 2 is assumed to be, but is not limited to, a hypertext transfer protocol (HTTP) or message queueing telemetry transport (MQTT). The format of the data transmitted from the communication unit Cfc to the information processing device 2 is assumed to be, but is not limited to, JSON (JavaScript (registered trademark) Object Notation), CSV (Comma Separated Values), etc. The communication unit Cfc may be configured to transmit data to the information processing device 2 via Wifi (registered trademark), Bluetooth (registered trademark), etc. The communication unit Cfc may be configured to transmit data to the information processing device 2 via wired communication.

[0026] The control unit Cnt is configured by a CPU (Central Processing Unit) or a programmable device (such as an FPGA (Field Programmable Gate Array) or a PLD (Programmable Logic Device)).

[0027] The control unit Cnt reads out software for controlling the operation of the fuel cell module FCM from the storage unit Sfc and executes it to obtain operation data indicating the operation state of the fuel cell module FCM at each sampling period corresponding to the version of the software, and controls the operation of the auxiliary device Au using the operation data so that the power output from the fuel cell stack FCS matches the target power generation. For example, the operation data may be a sensor value, a control variable, or abnormality information. The sensor value may be, for example, a detection value of a sensor that detects the current flowing through the fuel cell stack FCS, a detection value of a sensor that detects the voltage output from the fuel cell stack FCS, a detection value of a sensor that detects the flow rate of the oxidant gas or fuel gas flowing through the fuel cell stack FCS, or a detection value of a sensor that detects the temperature of the fuel cell stack FCS. The control variable may be, for example, the power generation time of the fuel cell stack FCS or the number of times the target power generation power of the fuel cell stack FCS has changed. The abnormality information may be, for example, a flag indicating that the current flowing through the fuel cell stack FCS has reached a current threshold or higher, or a flag indicating that the temperature of the fuel cell stack FCS has reached a temperature threshold or higher.

[0028] In addition, the control unit Cnt stores the operational data acquired at each sampling period according to the software version in the storage unit Sfc.

[0029] The control unit Cnt causes the communication unit Cfc to transmit the operation data stored in the storage unit Sfc to the information processing device 2 as analysis data in each communication cycle in association with identification information IDfc (first identification information) for identifying the fuel cell module FCM, identification information IDsp (second identification information) for identifying the configuration content (type) of the fuel cell module FCM, and identification information IDsm for identifying the version of the software executed by the control unit Cnt. The identification information IDsp is, for example, a model number indicating differences in the arrangement position, type, number, etc. of the components constituting the fuel cell module FCM. The control unit Cnt may transmit the analysis data one by one in each communication cycle, or may transmit multiple pieces of analysis data together. In addition, when the identification information (for example, serial number, manufacturing number, etc.) for identifying the communication unit Cfc is different for each corresponding fuel cell module FCM, the control unit Cnt may be configured to transmit the identification information of the communication unit Cfc to the information processing device 2 as the identification information IDfc in association with the operation data in each communication cycle in association with the operation data.

[0030] The information processing device 2 includes a storage unit S, a communication unit C, and an analysis unit An.

[0031] The storage unit S is configured with a non-volatile memory such as a ROM or a flash memory.

[0032] The communication section C receives the analysis data transmitted from each of the fuel cell modules FCM1, FCM2 via the base station B and the network N, and sends the received analysis data to the analysis section An.

[0033] The analysis unit An is configured with a CPU or a programmable device, and stores the analysis data sent from the communication unit C in the memory unit S.

[0034] Furthermore, when it is time to execute the analysis process, the analysis unit An selects an analysis pattern corresponding to the identification information IDsp linked to the fuel cell module FCM to be analyzed from among the multiple analysis patterns stored in the storage unit S. Note that the analysis unit An adds, deletes, or updates analysis patterns in response to a user operation.

[0035] Furthermore, when it is time to execute the analysis process, the analysis unit An selects the sampling period corresponding to the identification information IDsm linked to the fuel cell module FCM to be analyzed from among the multiple sampling periods stored in the storage unit S. Note that the analysis unit An adds, deletes, or updates the sampling period corresponding to the identification information IDsm in response to a user operation.

[0036] Furthermore, when it is time to execute an analysis process, the analysis unit An extracts, from the plurality of pieces of operational data stored in the storage unit S, the operational data corresponding to the identification information IDfc linked to the fuel cell module FCM to be analyzed.

[0037] Furthermore, when it is time to execute the analysis process, the analysis unit An analyzes the extracted operational data based on the selected analysis pattern and sampling period, and outputs the analysis results. The analysis results are used to detect signs of abnormalities in the fuel cell module FCM, and are used in the product development of next-generation fuel cell module FCMs.

[0038] FIG. 2 is a flowchart showing an example of the operation of the analysis unit An.

[0039] First, when it is time to execute the analysis process, the analysis unit An extracts the operation data corresponding to the analysis target period and the identification information IDfc linked to the fuel cell module FCM to be analyzed from among the multiple operation data stored in the storage unit S (step St1). The unit of the analysis target period may be, for example, hourly, daily, monthly, or annual, but is not particularly limited. The execution timing of the analysis process is, for example, a predetermined time on the day following the analysis target period when the analysis target period is in days, and is not particularly limited as long as it is any timing after the analysis target period.

[0040] Next, the analysis unit An selects an unselected analysis content from among a plurality of analysis contents pre-stored in the storage unit S, and selects an analysis pattern corresponding to the selected analysis content and the identification information IDsp linked to the fuel cell module FCM to be analyzed (step St2). Note that the analysis content is not particularly limited, and may be, for example, a total value, an average value, a maximum value, a minimum value, a time measurement, a count measurement, etc. of the operational data.

[0041] Next, if the analysis unit An determines that the analysis content selected in step St2 is time-related (step St3: Yes), it proceeds to step St4, and if it determines that the analysis content selected in step St2 is not time-related (step St3: No), it proceeds to step St6. Examples of analysis content related to time include high-load power generation time (total time when the output power of the fuel cell stack FCS is equal to or greater than a power threshold), total time when the detection value of a sensor at a specific location is equal to or greater than a threshold, time required to fill the fuel cell stack FCS with fuel gas, running period of the vehicle Ve, operation time of the loading device, key-on time of the vehicle Ve, etc.

[0042] In step St4, the analysis unit An selects, from among a plurality of sampling periods stored in the storage unit S, a sampling period that corresponds to the identification information IDsm linked to the fuel cell module FCM to be analyzed.

[0043] Next, the analysis unit An analyzes the operational data extracted in step St1 based on the analysis pattern selected in step St2 and the sampling period selected in step St4, and outputs the analysis result (step St5). For example, when outputting the analysis result, the analysis unit An stores the analysis result in the storage unit S, or displays the analysis result on a display (not shown).

[0044] On the other hand, in step St6, the analysis unit An analyzes the operational data extracted in step S1 based on the analysis pattern selected in step St2, and outputs the analysis result.

[0045] Next, if there is any unselected analysis content in the fuel cell module FCM to be analyzed (step St7: No), the analysis unit An executes steps St2 to St7 again for the unselected analysis content.

[0046] Next, after selecting all the analysis contents for the fuel cell module FCM to be analyzed, if the analysis has not been completed for all fuel cell modules FCM (step St7: Yes, step St8: No), the analysis unit An executes steps St1 to St8 again for the unanalyzed fuel cell modules FCM.

[0047] Then, when the analysis of all fuel cell modules FCM is completed (step St8: Yes), the analysis unit An ends the analysis process.

[0048] <Example> Here, the following 1) to 11) are assumed. 1) The identification information IDfc associated with the fuel cell module FCM1 is set to “1”. 2) The identification information IDsp associated with the fuel cell module FCM1 is set to “XXXX”. 3) The sampling period corresponding to the identification information IDsm "1.00" associated with the fuel cell module FCM1 is set to "2 [s]". 4) The sampling period corresponding to the identification information IDsm "1.01" associated with the fuel cell module FCM1 is set to "1 [s]". 5) The identification information IDfc associated with the fuel cell module FCM2 is set to “2”. 6) The identification information IDsp associated with the fuel cell module FCM2 is set to “YYYY”. 7) The sampling period corresponding to the identification information IDsm "1.00" associated with the fuel cell module FCM2 is set to "3 [s]". 8) The sampling period corresponding to the identification information IDsm "1.01" associated with the fuel cell module FCM2 is set to "1 [s]". 9) The analysis period will be "2022 / 9 / 7" (2022 / 9 / 7 0:00:00~23:59:59). 10) The multiple analysis contents pre-stored in the memory unit S are “high-load power generation time,” “XX total value,” and “△△ average value.” 11) The operational data has five storage areas: a "timestamp" storage area, a "DataA" storage area, a "DataB" storage area, a "DataC" storage area, and a "DataD" storage area.

[0049] Figure 3(a) is a diagram showing nine pieces of operational data corresponding to the analysis period "2022 / 9 / 7" and identification information IDfc "1" among the multiple pieces of operational data stored in the memory unit S. That is, "2022 / 9 / 7 20:00:03 to 2022 / 9 / 7 20:00:14" are stored in the "timestamp" storage area of ​​the nine pieces of operational data shown in Figure 3(a), and "1" is stored in each of the nine pieces of identification information IDfc shown in Figure 3(a). In addition, the nine identification information IDsp shown in Figure 3(a) each stores "XXXX", the nine identification information IDsm shown in Figure 3(a) stores "1.00" or "1.01", the "DataA" storage area of ​​the nine operational data shown in Figure 3(a) stores "100" or "50", the "DataB" storage area of ​​the nine operational data shown in Figure 3(a) stores "1" or "2", the "DataC" storage area of ​​the nine operational data shown in Figure 3(a) stores "1" or "0", and the "DataD" storage area of ​​the nine operational data shown in Figure 3(a) stores "9.0" or "8.5", etc.

[0050] Also, Figure 3(b) is a diagram showing nine pieces of operational data corresponding to the analysis period "2022 / 9 / 7" and identification information IDfc "2" among the multiple pieces of operational data stored in the memory unit S. That is, "2022 / 9 / 7 21:30:31" to "2022 / 9 / 7 21:30:42" are stored in the "timestamp" storage area of ​​the nine pieces of operational data shown in Figure 3(b), and "2" is stored in each of the nine pieces of identification information IDfc shown in Figure 3(b). In addition, the nine identification information IDsp shown in Figure 3(b) stores "YYYY", the nine identification information IDsm shown in Figure 3(b) stores "1.00" or "1.01", the "DataA" storage area of ​​the nine operational data shown in Figure 3(b) stores "10" or "30", the "DataB" storage area of ​​the nine operational data shown in Figure 3(b) stores "1" or "2", the "DataC" storage area of ​​the nine operational data shown in Figure 3(b) stores "1" or "0", and the "DataD" storage area of ​​the nine operational data shown in Figure 3(b) stores "9.0" or "8.5", etc.

[0051] Fig. 4 is a diagram showing an example of the correspondence between analysis contents, analysis patterns, identification information IDsp, identification information IDsm, and sampling periods. The analysis unit An refers to the information shown in Fig. 4 to select an appropriate analysis pattern according to the operation control contents or configuration contents of the fuel cell module FCM or an appropriate sampling period according to the sampling period of the fuel cell module FCM, and performs analysis on the operation data shown in Fig. 3(a) or Fig. 3(b) using the selected analysis pattern and sampling period.

[0052] In the example shown in FIG. 4, the analysis content "high load power generation time" and the identification information IDsp "XXXX" correspond to the analysis pattern "high load power generation time / analysis pattern 1". In addition, the identification information IDsp "XXXX" and the identification information IDsm "1.00" correspond to the sampling period "2 [s]", and the identification information IDsp "XXXX" and the identification information IDsm "1.01" correspond to the sampling period "1 [s]". In "high load power generation time / analysis pattern 1", the analysis target is "DataD≧8.0" and the aggregation method is "time measurement". In other words, in "high load power generation time / analysis pattern 1", the analysis result is the result of time measurement obtained by multiplying the number of operation data that meets the condition "DataD≧8.0" among the nine operation data shown in FIG. 3(a) or FIG. 3(b) by the sampling period.

[0053] 4, the analysis content "high-load power generation time" and the identification information IDsp "YYYY" correspond to the analysis pattern "high-load power generation time / analysis pattern 1." The identification information IDsp "YYYY" and the identification information IDsm "1.00" correspond to the sampling period "3 [s]," and the identification information IDsp "YYYY" and the identification information IDsm "1.01" correspond to the sampling period "1 [s]."

[0054] In the example shown in Fig. 4, the analysis content "XX Total Value" and the identification information IDsp "XXXX" correspond to "XX Total Value / Analysis Pattern 1" as an analysis pattern. "XX Total Value / Analysis Pattern 1" sets the analysis target to "All", the calculation method to "DataA x DataB ÷ 10", and the aggregation method to "Total". In other words, "XX Total Value / Analysis Pattern 1" calculates "DataA x DataB ÷ 10" for "All" of the nine pieces of operational data shown in Fig. 3(a) or Fig. 3(b), and sets the "total" of each calculation result as the analysis result.

[0055] In the example shown in Fig. 4, the analysis content "△△ average value" and the identification information IDsp "XXXX" correspond to the analysis pattern "△△ average value / analysis pattern 3". "△△ average value / analysis pattern 3" sets the analysis target as "DataC is equal to 1", the calculation method as "DataA x DataB ÷ 10", and the aggregation method as "average". In other words, "△△ average value / analysis pattern 3" calculates "DataA x DataB ÷ 10" for the operational data that meets the condition "DataC is equal to 1" among the nine operational data shown in Fig. 3(a) or Fig. 3(b), and sets the "average" of each calculation result as the analysis result.

[0056] In the example shown in FIG. 4, the analysis content "XX Total Value" and the identification information IDsp "YYYY" correspond to "XX Total Value / Analysis Pattern 3" as an analysis pattern. "XX Total Value / Analysis Pattern 3" sets the analysis target as "DataC is equal to 1", the calculation method as "DataA x DataB ÷ 10", and the aggregation method as "total". In other words, "XX Total Value / Analysis Pattern 3" calculates "DataA x DataB ÷ 10" for the operational data that satisfies the condition "DataC is equal to 1" among the nine operational data shown in FIG. 3(a) or FIG. 3(b), and sets the "total" of each calculation result as the analysis result.

[0057] In the example shown in Fig. 4, the analysis content "△△ average value" and the identification information IDsp "YYYY" correspond to the analysis pattern "△△ average value / analysis pattern 2". "△△ average value / analysis pattern 2" sets the condition of the operational data to be analyzed as "DataC is equal to 0", the calculation method as "DataA x DataB ÷ 10", and the aggregation method as "average". In other words, "△△ average value / analysis pattern 2" calculates "DataA x DataB ÷ 10" for the operational data that meets the condition "DataC is equal to 0" among the nine operational data shown in Fig. 3(a) or Fig. 3(b), and sets the "average" of each calculation result as the analysis result.

[0058] Moreover, Figure 5(a) is a diagram showing an example of an analysis result corresponding to the analysis content "high-load power generation time" and the identification information IDsp "XXXX", Figure 5(b) is a diagram showing an example of an analysis result corresponding to the analysis content "○○ total value" and the identification information IDsp "XXXX", and Figure 5(c) is a diagram showing an example of an analysis result corresponding to the analysis content "△△ average value" and the identification information IDsp "XXXX".

[0059] Moreover, Figure 6(a) is a diagram showing an example of an analysis result corresponding to the analysis content "high-load power generation time" and the identification information IDsp "YYYY", Figure 6(b) is a diagram showing an example of an analysis result corresponding to the analysis content "XX total value" and the identification information IDsp "YYYY", and Figure 6(c) is a diagram showing an example of an analysis result corresponding to the analysis content "△△ average value" and the identification information IDsp "YYYY".

[0060] In such a case, when the timing for executing the analysis process arrives, the analysis unit An first extracts the nine operating data shown in Figure 3(a) from the multiple operating data stored in the memory unit S as operating data corresponding to the analysis period "2022 / 9 / 7" and the identification information IDfc "1" linked to the fuel cell module FCM1 to be analyzed.

[0061] Next, the analysis unit An selects the analysis content "high-load power generation time" from the three analysis contents shown in Figure 4, and selects "high-load power generation time / analysis pattern 1" as the analysis pattern corresponding to the selected analysis content "high-load power generation time" and the identification information IDsp "XXXX" linked to the fuel cell module FCM1 to be analyzed.

[0062] Next, when the analysis unit An determines that the analysis content "high-load power generation time" is an analysis content related to time, it selects "2 [s]" as the sampling period corresponding to the identification information IDsp "XXXX" linked to the fuel cell module FCM1 to be analyzed and the identification information IDsm "1.00" linked to the fuel cell module FCM1 to be analyzed, and selects "1 [s]" as the sampling period corresponding to the identification information IDsp "XXXX" linked to the fuel cell module FCM1 to be analyzed and the identification information IDsm "1.01" linked to the fuel cell module FCM1 to be analyzed.

[0063] Next, the analysis unit An performs an analysis on the nine pieces of operation data shown in Fig. 3(a) based on "high-load power generation time / analysis pattern 1" and "2[s], 1[s]". That is, the analysis unit An outputs the sum of "6[s]" obtained by multiplying the number of pieces of operation data "3" corresponding to "DataD≧8.0" and the identification information IDsm "1.00" among the nine pieces of operation data shown in Fig. 3(a) by the sampling period "2[s]" and the number of pieces of operation data "2" corresponding to "DataD≧8.0" and the identification information IDsm "1.01" among the nine pieces of operation data shown in Fig. 3(a) by the sampling period "1[s]", as shown in Fig. 5(a), which is "8[s]".

[0064] Next, the analysis unit An selects the analysis content "XX total value" from the three analysis contents shown in Figure 4, and selects "XX total value / analysis pattern 1" as the analysis pattern corresponding to the selected analysis content "XX total value" and the identification information IDsp "XXXX" linked to the fuel cell module FCM1 to be analyzed.

[0065] Next, if the analysis unit An determines that the analysis content "XX total value" is not related to time, it performs analysis on the nine operational data shown in Figure 3(a) based on "XX total value / analysis pattern 1". That is, the analysis unit An calculates "DataA×DataB÷10" for "all" of the nine pieces of operational data shown in FIG. 3(a) to obtain the calculation results "10=100×1÷10", "10=50×2÷10", "24=80×3÷10", "8=20×4÷10", "50=100×5÷10", "54=90×6÷10", "28=40×7÷10", "32=40×8÷10", and "90=100×9÷10". Then, by calculating the sum of these calculation results, the calculation result "306=10+10+24+8+50+54+28+32+90" is obtained, and the calculation result "306" is output as the analysis result corresponding to the analysis content "XX total value" and the identification information IDsp "XXXX", as shown in FIG. 5(b).

[0066] Next, the analysis unit An selects the analysis content "△△ average value" from the three analysis contents shown in Figure 4, and selects "△△ average value / analysis pattern 3" as the analysis pattern corresponding to the selected "△△ average value" and the identification information IDsp "XXXX" linked to the fuel cell module FCM1 to be analyzed.

[0067] Next, when the analysis unit An determines that the analysis content "△△ average value" is not analysis content related to time, it performs analysis on the nine operational data shown in Fig. 3(a) based on "△△ average value / analysis pattern 3". That is, the analysis unit An calculates "DataA x DataB ÷ 10" for each of the nine operational data shown in Fig. 3(a) that satisfy the condition "DataC is equal to 1", and obtains the calculation results "10 = 100 x 1 ÷ 10", "10 = 50 x 2 ÷ 10", "8 = 20 x 4 ÷ 10", "50 = 100 x 5 ÷ 10", and "28 = 40 x 7 ÷ 10". Then, by calculating the average value of each calculation result, it obtains the counting result "21.2 = (10 + 10 + 8 + 50 + 28) ÷ 5", and outputs the counting result "21.2" as the analysis result corresponding to the analysis content "△△ average value" and the identification information IDsp "XXXX" as shown in Fig. 5(c).

[0068] Next, after the analysis unit An determines that all three analysis contents shown in Figure 4 have been selected for the fuel cell module FCM1, if it determines that the analysis for the fuel cell module FCM2 has not been completed, it extracts the nine operating data shown in Figure 3(b) from the multiple operating data stored in the memory unit S as operating data corresponding to the analysis period "2022 / 9 / 7" and the identification information IDfc "2" linked to the fuel cell module FCM2 to be analyzed.

[0069] Next, the analysis unit An selects the analysis content "high-load power generation time" from the three analysis contents shown in Figure 4, and selects "high-load power generation time / analysis pattern 1" as the analysis pattern corresponding to the selected analysis content "high-load power generation time" and the identification information IDsp "YYYY" linked to the fuel cell module FCM2 to be analyzed.

[0070] Next, when the analysis unit An determines that the analysis content "high-load power generation time" is an analysis content related to time, it selects "3 [s]" as the sampling period corresponding to the identification information IDsp "YYYY" linked to the fuel cell module FCM2 to be analyzed and the identification information IDsm "1.00" linked to the fuel cell module FCM2 to be analyzed, and selects "1 [s]" as the sampling period corresponding to the identification information IDsp "YYYY" linked to the fuel cell module FCM2 to be analyzed and the identification information IDsm "1.01" linked to the fuel cell module FCM2 to be analyzed.

[0071] Next, the analysis unit An performs an analysis on the nine pieces of operation data shown in Fig. 3(b) based on "high-load power generation time / analysis pattern 1" and "3[s], 1[s]". That is, the analysis unit An outputs the sum of "9[s]" obtained by multiplying the number of pieces of operation data "3" corresponding to "DataD≧8.0" and the identification information IDsm "1.00" among the nine pieces of operation data shown in Fig. 3(b) by the sampling period "3[s]" and the number of pieces of operation data "5" corresponding to "DataD≧8.0" and the identification information IDsm "1.01" among the nine pieces of operation data shown in Fig. 3(b) by the sampling period "1[s]", as shown in Fig. 6(a).

[0072] Next, the analysis unit An selects the analysis content "XX total value" from the three analysis contents shown in Figure 4, and selects "XX total value / analysis pattern 3" as the analysis pattern corresponding to the selected "XX total value" and the identification information IDsp "YYYY" linked to the fuel cell module FCM2 to be analyzed.

[0073] Next, if the analysis unit An determines that the analysis content “XX total value” is not related to time, it performs analysis on the nine operational data shown in Figure 3(b) based on “XX total value / analysis pattern 3”. That is, the analysis unit An calculates "DataA × DataB ÷ 10" for each of the nine operational data shown in FIG. 3(b) that satisfy the condition "DataC is equal to 1", to obtain the calculation results "1=10×1 ÷ 10", "6=30×2 ÷ 10", "27=90×3 ÷ 10", "25=50×5 ÷ 10", "59.5=85×7 ÷ 10", and "60=75×8 ÷ 10", and then calculates the sum of these calculation results to obtain the aggregated result "178.5=1 + 6 + 27 + 25 + 59.5 + 60", and outputs the aggregated result "178.5" as the analysis result corresponding to the analysis content "Total value of XX" and the identification information IDsp "YYYY", as shown in FIG. 6(b).

[0074] Next, the analysis unit An selects the analysis content "△△ average value" from the three analysis contents shown in Figure 4, and selects "△△ average value / analysis pattern 2" as the analysis pattern corresponding to the selected analysis content "△△ average value" and the identification information IDsp "YYYY" linked to the fuel cell module FCM2 to be analyzed.

[0075] Next, when the analysis unit An determines that the analysis content "△△ average value" is not related to time, it performs an analysis on the nine operational data shown in Fig. 3(b) based on "△△ average value / analysis pattern 2". That is, the analysis unit An calculates "DataA x DataB ÷ 10" for each of the nine operational data shown in Fig. 3(b) that satisfy the condition "DataC is equal to 0", and obtains the calculation results "40 = 100 x 4 ÷ 10", "60 = 100 x 6 ÷ 10", and "90 = 100 x 9 ÷ 10". Then, by calculating the average value of each calculation result, it obtains the count result "63.3 ≒ (40 + 60 + 90) ÷ 3", and outputs the count result "63.3" as the analysis result corresponding to the analysis content "△△ average value" and the identification information IDsp "YYYY" as shown in Fig. 6(c).

[0076] Then, when the analysis unit An determines that all of the analysis contents shown in Figure 4 have been selected for the fuel cell module FCM2 and that the analysis has been completed for all fuel cell modules FCM1 and FCM2, it terminates the analysis process corresponding to the analysis period "2022 / 9 / 7".

[0077] Thus, in the embodiment of the operation management system 1, the identification information IDfc, identification information IDsp, identification information IDsm, and operation data are associated with each other and transmitted from the fuel cell module FCM to the information processing device 2, and the information processing device 2 analyzes the operation data corresponding to the identification information IDfc based on the analysis pattern corresponding to the identification information IDsp and the sampling period corresponding to the identification information IDsm.

[0078] As a result, even if the sampling period for operational data differs between fuel cell modules FCM1, FCM2 depending on the software version, it is possible to analyze the operational data according to the sampling period of each of the fuel cell modules FCM1, FCM2, and therefore there is no need to adjust the sampling period of each of the fuel cell modules FCM1, FCM2 to a minimum sampling period. As a result, one of the sampling periods of the fuel cell modules FCM1, FCM2 can be made longer than the other, which reduces the amount of data transmitted from the fuel cell modules FCM1, FCM2 to the information processing device 2 and suppresses increases in running costs associated with software updates.

[0079] Furthermore, the operation management system 1 according to the embodiment is configured such that the sampling period (the sampling period shown in FIG. 4) corresponding to the identification information IDSM is added, deleted, or updated in response to a user operation in the information processing device 2.

[0080] This allows the user to easily add, delete, or update sampling periods.

[0081] Furthermore, in the embodiment of the operation management system 1, the identification information IDfc, the identification information IDsp, and the operation data are associated with each other and transmitted from the fuel cell module FCM to the information processing device 2, and the information processing device 2 is configured to analyze the operation data corresponding to the identification information IDfc based on the analysis pattern corresponding to the identification information IDsp.

[0082] As a result, even if the operational control contents or configuration contents differ for each fuel cell module FCM, the operational data corresponding to the fuel cell module FCM being analyzed can be analyzed using an appropriate analysis pattern according to the operational control contents or configuration contents of the fuel cell module FCM being analyzed.

[0083] Furthermore, the operation management system 1 according to the embodiment is configured such that the analysis patterns (analysis patterns shown in FIG. 4) are added, deleted, or updated in response to user operations in the information processing device 2.

[0084] This allows the user to easily add, delete, or update analysis patterns.

[0085] Moreover, the operation management system 1 according to the embodiment is configured to analyze operation data corresponding to an analysis target period in the information processing device 2.

[0086] This makes it possible to analyze, for example, operational data collected in one day for each of the fuel cell modules FCM1 and FCM2.

[0087] Furthermore, the fuel cell module FCM of the embodiment is configured to transmit operational data to the information processing device 2 using an existing communication terminal.

[0088] This eliminates the need to develop new communication terminals or install new base stations for transmitting and receiving data to and from the developed communication terminals, thereby suppressing increases in costs for building the operation management system 1.

[0089] The present invention is not limited to the above-described embodiment, and various improvements and modifications are possible without departing from the gist of the present invention.

[0090] For example, as in a modified example of the operation management system 1 shown in Fig. 7, the fuel cell modules FCM1 and FCM2 may be provided in stationary generators such as an industrial stationary generator, a home stationary generator, or an emergency stationary generator. The load Lo shown in Fig. 7 is, for example, an industrial machine or a home appliance. [Explanation of symbols]

[0091] 1 Operation management system 2. Information processing equipment FCM1, FCM2 fuel cell modules B base station N Network FCS Fuel Cell Stack Au Auxiliary Equipment Sfc storage Cfc Communications Department Cnt Control section C. Communications Department S storage section An analysis department Lo load Vehicle Ge stationary generator

Claims

1. An operation management system including a plurality of fuel cell modules and an information processing device, the plurality of fuel cell modules transmit to the information processing device first identification information for identifying the fuel cell module, second identification information for identifying the configuration of the fuel cell module, third identification information for identifying a version of software used to control the operation of the fuel cell module, and operation data indicating the operating state of the fuel cell module in correspondence with one another; The information processing device analyzes the operational data corresponding to the first identification information based on an analysis pattern corresponding to the second identification information and a sampling period corresponding to the third identification information. Operation management system.

2. The operation management system according to claim 1 , The information processing device adds, deletes, or updates a sampling period corresponding to the third identification information in response to a user operation. Operation management system.

3. The operation management system according to claim 1 , The plurality of fuel cell modules acquire the operation data at the sampling period. Operation management system.

4. a communication unit that receives first identification information for identifying the fuel cell module, second identification information for identifying the configuration of the fuel cell module, third identification information for identifying the version of software used to control the operation of the fuel cell module, and operation data indicating the operating status of the fuel cell module, each of which is transmitted from a plurality of fuel cell modules; an analysis unit that analyzes the operational data corresponding to the first identification information based on an analysis pattern corresponding to the second identification information and a sampling period corresponding to the third identification information; An information processing device comprising:

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