JP5463805B2 - Data analysis order determination apparatus and data analysis order determination program - Google Patents

Description

  The present invention relates to a data analysis order determination device and a data analysis order determination program.

  The time that an artificial satellite orbiting around the earth (earth orbiting satellite) can communicate directly with a ground station is a part of the time required for the artificial satellite to make one round of the earth (for example, about 100 minutes) (for example, about 10 minutes) ). Therefore, artificial satellites accumulate data collected from time to time, such as data indicating satellite status (orbit control, etc.) and observation data (cosmic ray status, etc.) collected during direct communication with ground stations, The data is sent to the ground station as long as communication is possible.

  On the other hand, the ground station has conventionally detected anomalies by analyzing the accumulated data collected in the order received, that is, in time series. For this reason, when data indicating an abnormal state is included in the collected data received later, it takes time to analyze the data, and it may take a long time to detect the abnormal state.

  Therefore, recently, a technique such as Patent Document 1 has appeared as a data processing method for the purpose of detecting an abnormal state at an early stage. The technique described in Patent Document 1 is a technique for transmitting position data recorded by a vehicle to a center in order of new recording time in order to quickly identify an accident position in the event of a vehicle accident.

Japanese Patent Laid-Open No. 11-250381

  However, some abnormalities are likely to occur after or during the occurrence of a specific event (event), so if the accumulated data is analyzed in order of newest time, the detection of the abnormality is rather delayed. There is a risk that.

  Therefore, the present invention has been made in view of the above problems, and an object thereof is to provide a data analysis order determination apparatus and a data analysis order determination program that can efficiently perform anomaly analysis of an artificial satellite.

The data analysis order determination device described in this specification collects collected data collected by the artificial satellite while it cannot communicate with the artificial satellite when the communication with the artificial satellite becomes possible. Data acquisition means, event information acquisition means for acquiring information on an event that occurred while the collected data was being collected, and an abnormality detection rate in the artificial satellite when the event occurred in the past. An abnormality detection rate calculating means for calculating each type of data, and an order determining means for determining the analysis order of the collected data based on the abnormality detection rate.

The data analysis order determination program described in the present specification collects collected data collected in the artificial satellite while the computer cannot communicate with the artificial satellite when communication with the artificial satellite becomes possible. and data acquisition means for acquiring, the event information acquisition unit for obtaining information on events that occurred while the collected data is being collected, the abnormality detection rate in the artificial satellite when the event has occurred in the past, the It functions as an abnormality detection rate calculation unit that calculates for each type of event, and an order determination unit that determines the analysis order of the collected data based on the abnormality detection rate.

  The data analysis order determination apparatus and the data analysis order determination program described in the present specification have an effect that the abnormality analysis of the artificial satellite can be efficiently performed.

It is a block diagram which shows the structure of the abnormality monitoring system which concerns on one Embodiment. FIG. 2A is a flowchart showing the process of the event collecting unit, and FIG. 2B is a flowchart showing the process of the accumulated data collecting unit. It is a figure which shows an event holding table. It is a figure which shows a stored data management table. FIG. 5A and FIG. 5B are diagrams illustrating an abnormality detection rate holding table for each event. It is a flowchart (the 1) which shows a process of the accumulation | storage data abnormality monitoring part. It is a flowchart (the 2) which shows a process of the accumulation | storage data abnormality monitoring part. It is a figure which shows the event holding table of FIG. 3 typically in time series. It is a figure which shows a stored data management table. It is a figure which shows the processing order of accumulation | storage data. It is a figure which shows the diagnostic process at the time of acquiring new accumulation | storage data. It is a figure which shows the state by which the event holding table was updated. It is a figure which shows the state by which the accumulation | storage data management table was updated. It is a figure which shows the other example 1 of one Embodiment. It is a figure which shows the other example 2 of one Embodiment.

  Hereinafter, an embodiment of a data analysis order determination device will be described in detail with reference to FIGS. FIG. 1 is a block diagram showing a configuration of an abnormality monitoring system 100 including a data analysis order determination device.

  As shown in FIG. 1, the abnormality monitoring system 100 is a system that receives data transmitted from the artificial satellite 200 via the receiving facility 300, analyzes the data, and monitors the abnormality of the artificial satellite. The artificial satellite 200 is, for example, an earth-orbiting satellite and circulates around the earth in, for example, about 100 minutes. Further, it is assumed that communication can be performed between the artificial satellite 200 and the receiving facility 300 on the earth only for about 10 minutes out of 100 minutes that go around the earth.

  Here, in a state where communication with the receiving facility 300 is possible, the artificial satellite 200 continues to transmit data acquired at any time (hereinafter referred to as “real-time data”) to the receiving facility 300. In addition, when the satellite 200 cannot communicate with the receiving facility 300, the artificial satellite 200 continues to accumulate data acquired during that time, and when the communication becomes possible, the data (hereinafter referred to as “accumulated data”). Are collectively transmitted to the receiving facility 300. The data acquired by the artificial satellite 200 is various data such as detection data of the state of the on-board equipment, that is, voltage, current, temperature, and observation data of an observation sensor provided in the artificial satellite.

  The abnormality monitoring system 100 includes a real-time data collection unit 12, a real-time data abnormality monitoring unit 14, a stored data collection unit 22, a stored data abnormality monitoring unit 24, an abnormality monitoring result notification unit 16, an event collection unit 30, Is provided. Further, the abnormality monitoring system 100 includes various databases and tables. Specifically, the abnormality monitoring system 100 includes a database 42 for real-time data and a database 44 for diagnostic knowledge data. The abnormality monitoring system 100 also includes an accumulated data management table 46, an accumulated data database 48, an event holding table 50, and an event-specific anomaly detection rate holding table 52.

  The real-time data collection unit 12 receives real-time data at any time via the receiving facility 300 and collects the real-time data in the database 42.

  The real-time data abnormality monitoring unit 14 analyzes the real-time data stored in the database 42 using the diagnostic knowledge data stored in the database 44. Thereby, the real-time data abnormality monitoring unit 14 diagnoses the artificial satellite 200 and detects the abnormality of the artificial satellite 200. Note that the real-time data abnormality monitoring unit 14 analyzes the order in which the real-time data is received, that is, in time series.

  The accumulated data collection unit 22 collectively receives accumulated data accumulated in the artificial satellite 200 when communication between the artificial satellite 200 and the receiving facility 300 is not possible, updates the accumulated data management table 46, and accumulates data in the database 48. Store.

  The accumulated data abnormality monitoring unit 24 includes a data analysis unit 242, an abnormality detection rate calculation unit 244, and an order determination unit 246. The accumulated data abnormality monitoring unit 24 reads the diagnostic knowledge data, the accumulated data management table 46, the event holding table 50, and the event-specific abnormality detection rate holding table 52 stored in the database 44. Then, by analyzing the accumulated data stored in the database 48 using these data and tables, the artificial satellite 200 is diagnosed and an abnormality of the artificial satellite 200 is detected. The accumulated data management table 46 and the event-specific abnormality detection rate holding table 52 are also updated. In addition, each process part (242,244,246) of the accumulation data abnormality monitoring part 24 and the specific processing content by each structure part are mentioned later.

  The abnormality monitoring result notification unit 16 includes a display, a speaker, and the like. The abnormality monitoring result notification unit 16 receives the diagnostic results in the real-time data abnormality monitoring unit 14 and the accumulated data abnormality monitoring unit 24 and notifies the operator of the diagnostic results.

  The event collection unit 30 receives information on commands executed by the artificial satellite 200 from the satellite administration system 62, receives orbit information of the artificial satellite 200 from the orbit determination system 64, and receives the space weather information from the space weather information providing unit 66. Receive information. Further, the event collection unit 30 stores the received information in the event holding table 50.

  The real-time data database 42 stores real-time data received from the artificial satellite 200. The diagnostic knowledge data database 44 stores in advance diagnostic knowledge data used for diagnosis of abnormality. The diagnostic knowledge data includes diagnostic rules and the like performed in the real-time data abnormality monitoring unit 14.

  The accumulated data management table 46 is a table for managing accumulated data. This table 46 is a table as shown in FIG. Specifically, the table 46 includes the data time of each accumulated data (the time when the data was acquired in the artificial satellite 200) and an abnormality monitoring processing flag indicating whether or not the data has been processed. Yes.

  Returning to FIG. 1, accumulated data received from the artificial satellite 200 is stored in the accumulated data database 48. The event holding table 50 is a table for managing events. The event holding table 50 is a table as shown in FIG. 3 and includes an event name and the start time and end time of the event.

  The event-specific abnormality detection rate holding table 52 is a table for managing the abnormality detection rate for each event. The abnormality detection rate holding table 52 for each event is a table as shown in FIG. 5A. Specifically, the event name, the total event occurrence time, the total number of detected abnormalities, and the first half total event abnormality detection Includes numbers. The total event occurrence time indicates, for example, how many events such as shade have occurred since the artificial satellite 200 started operation. The total number of detected abnormalities indicates how much abnormality has occurred in the artificial satellite 200 while an event such as a shade is occurring. Further, the total number of detected abnormalities in the first half of the event indicates how much abnormality has occurred in the artificial satellite 200 in the first half of the event.

  Next, the processing of the event collection unit 30 and the accumulated data collection unit 22 will be described in detail based on FIGS. 2 (a) and 2 (b).

  FIG. 2A is a flowchart showing the processing of the event collection unit 30. The event collection unit 30 repeats the process of FIG. 2A at predetermined time intervals. In step S10 of the flowchart of FIG. 2A, the event collection unit 30 collects event data, and stores the event start time and event end time in the event holding table 50 (see FIG. 3). FIG. 3 shows a case where an event that may affect the operation of the artificial satellite 200, such as a shade or a sun flare, is stored in the event holding table 50 as an event. The shade refers to the period when the artificial satellite enters the shadows of the earth and the moon and the sunlight reaches.

  Here, the artificial satellite 200 generates electricity using a solar cell owned by itself, stores the electric power in a battery, and operates in the shade when the electric power stored in the battery is entered. That is, if the power supply source from the solar cell to the battery cannot be switched smoothly when entering the shade, the operation of the artificial satellite 200 may be affected. In addition, the operation of the artificial satellite 200 may be affected even when the X-ray dose from the sun fluctuates greatly due to the occurrence of solar flares or sunspots. For this reason, in this embodiment, shade and solar flare are described as examples of events. The time when the shade occurs (start time and end time) can be calculated from the orbit information transmitted from the orbit determination system 64 of FIG. 1 and the space weather information transmitted from the space weather information providing unit 66. . Further, the occurrence information of solar flare can be obtained from the space weather information transmitted from the space weather information providing unit 66. An event other than shade or solar flare can be used as the event. For example, a time zone in which orbit control is performed, that is, a command execution time zone for starting and ending fuel injection, which can be obtained from command information of the satellite administration system 62, may be used as an event. Alternatively, other natural phenomena or control information may be used as events.

  FIG. 2B is a flowchart showing the processing of the accumulated data collection unit 22. The accumulated data collection unit 22 repeats the flowchart of FIG. 2B at a predetermined time interval (here, 2 seconds). In step S <b> 20 of FIG. 2B, the accumulated data collection unit 22 collects accumulated data, that is, stores the accumulated data in the database 48. Further, the accumulated data collection unit 22 holds the accumulated data time and the abnormality monitoring processed flag (in this case, the unprocessed state) in the accumulated data management table 46 (see FIG. 4) as management information of the accumulated data. In the accumulated data management table 46 of FIG. 4, the data time of the accumulated data is stored at intervals of 2 seconds from “2009/10/01 00:00:00” to “2009/10/01 20:00:00”. Has been. In addition, an unprocessed flag is associated with these times.

  Next, the processing of the accumulated data abnormality monitoring unit 24 shown in FIG. 1 will be described in detail based on FIG. 6 and FIG. 6 and 7 are flowcharts showing the processing of the accumulated data abnormality monitoring unit 24. In the processing of FIGS. 6 and 7, the analysis order of data collected at the time of event occurrence is determined based on the detection rate of the abnormality that occurred in the artificial satellite when the event occurred in the past, and data analysis is performed according to the analysis order. Is to do.

  First, in step S30 of FIG. 6, the order determining unit 246 of the accumulated data abnormality monitoring unit 24 refers to the collected data time in the accumulated data management table 46 (FIG. 4). And the event which generate | occur | produced in the time slot | zone containing all the data time is extracted from the event holding table 50 (FIG. 3). In addition, the data time when the unprocessed flag is set is extracted from the accumulated data management table 46 among the data acquired while the event is occurring.

  Specifically, first, the order determination unit 246 starts from the time zone stored in the stored data management table 46, “2009/10/01 00:00:00” in FIG. Event corresponding to the time period up to “0:00” is extracted from the event holding table 50. Here, when the event holding table 50 of FIG. 3 is schematically shown in time series as shown in FIG. 8, in step S30, the accumulated data abnormality monitoring unit 24 performs each event (three times) shown in FIG. The shade and one solar flare). Then, the order determination unit 246 extracts, from the accumulated data management table 46, time zones in which data has not yet been processed among the time zones of these events. At this stage, since all data is unprocessed, all the data corresponding to the time zone when the event occurred is extracted. Hereinafter, the time zone extracted in step S30 is referred to as “abnormality monitoring candidate time zone”.

  Returning to FIG. 6, in the next step S <b> 32, the order determination unit 246 of the accumulated data abnormality monitoring unit 24 determines whether there is an event in which a past abnormality is detected in the abnormality monitoring candidate time zone. The case where the determination in step S32 is negative means that no abnormality has been detected in the past in the time zone in which the event has occurred. For this reason, many cases where the determination is negative in step S32 are when a new event occurs or when the system is newly activated. Here, it is assumed that abnormality has been detected in the past for both shade and solar flare. Therefore, it is assumed that the determination in step S32 is affirmed and the process proceeds to step S34.

  In step S34, the abnormality detection rate calculating unit 244 of the accumulated data abnormality monitoring unit 24 displays the total abnormality detection rate and the first half total abnormality detection rate of the event where the past abnormality has been detected, for each event abnormality detection rate holding table 52 (FIG. 5). (A)). Specifically, the abnormality detection rate calculation unit 244 calculates the total abnormality detection rate by dividing the total abnormality detection number by the total event occurrence time, and calculates the total first half event abnormality detection number by half of the total event occurrence time. Divide the total event detection rate for the first half of the event. In the present embodiment, as a result of the above calculation, as shown in FIG. 8, the shaded abnormality detection rate is 0.04, the first half event abnormality detection rate is 0.07, and the solar flare abnormality detection rate is 0. .02 and the first half event total abnormality detection rate is 0.02.

  Returning to FIG. 6, in the next step S40, the order determination unit 246 of the accumulated data abnormality monitoring unit 24 identifies the highest detection rate. In the present embodiment, the shaded first half total abnormality detection rate is specified as the highest detection rate.

  Next, in step S42, the order determination unit 246 of the accumulated data abnormality monitoring unit 24 determines whether or not the highest detection rate is only the first half total abnormality detection rate. If the determination here is affirmed, the process proceeds to step S44. If the determination is negative, the process proceeds to step S46. In the present embodiment, only the shaded event first half total abnormality detection rate is the highest value, so the determination in step S42 is affirmed and the process proceeds to step S44. In step S44, the order determination unit 246 monitors the current abnormality monitoring for the event having the highest abnormality detection rate, the event closest to the current time, and the unprocessed time zone closest to the event start time. Set as processing time zone. Specifically, the time period (time “18:30:30”) of “diagnosis process (1)” shown in FIG. 8 is set as the current abnormality monitoring process time period. Thereafter, the process proceeds to step S48 in FIG.

  Thereafter, when the process proceeds to step S48 in FIG. 7, the accumulated data abnormality monitoring unit 24 (data analysis unit 242) performs abnormality monitoring processing in the set time zone. In the present embodiment, in one abnormality monitoring process, the abnormality monitoring process is executed using data for a preset time (for example, 5 minutes). In addition, when step S44 of FIG. 6 is passed, the abnormality monitoring is performed by analyzing data for a preset time (5 minutes) in the direction in which the time advances from the current abnormality monitoring processing time zone. In the present embodiment, the accumulated data abnormality monitoring unit 24 (data analysis unit 242) analyzes the data in the range from the time “18:30:30” to “18:34:58” and performs abnormality monitoring.

  Next, in step S50, the abnormality detection rate calculation unit 244 of the accumulated data abnormality monitoring unit 24 updates the event-specific abnormality detection rate holding table 52. For example, when an abnormality is detected in step S48, as shown in FIG. 5B, 1 is added to the total abnormality detection number and the first half event abnormality detection number. In addition, a preset time (5 minutes) is added to the total event occurrence time. In this case, not only the shade but also the solar flare occurs at the time “18:30:30” in FIG. 8, so in FIG. 5B, not only the number of detected shades but also the number of detected solar flares. 1 is added.

  Next, in step S52 in FIG. 7, the abnormality detection rate calculation unit 244 of the accumulated data abnormality monitoring unit 24 sets the time zone in which the abnormality monitoring process is currently performed as the abnormality monitoring process completed, and the abnormality monitoring process of the accumulated data management table has been completed. Update the flag to processed. Specifically, the abnormality detection rate calculation unit 244 processes the flags in the range from the time “18:30:30” to “18:34:58”, as indicated by the reference S in FIG. Update to completed.

  As described above, when all the processes in FIGS. 6 and 7 are completed, the process is started again from step S30, and the same process as described above is repeated. That is, the abnormality monitoring process using the data from the time “18:35:00” to the time “18:39:58”, which is the continuation, is executed, and thereafter the abnormality monitoring process is similarly repeated.

  By executing such processing, the accumulated data abnormality monitoring unit 24 is in the order indicated by the circled numbers in FIG. 10 until the next accumulated data is transmitted from the artificial satellite 200. Data is analyzed and abnormality monitoring processing is executed. Note that the processing of the circled numbers 1 to 3 in FIG. 10 has gone through step S44 in FIG. 6 and the circled numbers 4 and 5 have gone through step S46 in FIG. ing. The process of step S46 will be described later.

  After that, for example, as shown in FIG. 11, when the accumulated data collection unit 22 newly receives data up to the time “23:59:58”, the accumulated data collection unit 22 6 and 7 are executed using the received data and the newly received stored data. Note that, at the time “24:00:00”, the event collection unit 30 adds “shade” shown at the bottom of FIG. 12 as a new event to the event holding table 50. Further, the accumulated data collection unit 22 adds a new data time and an abnormality monitoring processed flag, which are shown in FIG. 13 with hatching, to the accumulated data management table 46.

  In this case, when the processing of FIG. 6 and FIG. 7 is executed, the time zone “23:30” of the diagnostic processing (2) shown in FIG. 11 is set as the current abnormality monitoring processing time zone (step S44). . Thereafter, the same processing as described above is executed.

  As described above, the result of the abnormality diagnosis performed in the accumulated data abnormality monitoring unit 24 is transmitted to the abnormality monitoring result notifying unit 16 shown in FIG. Then, the abnormality monitoring result notifying unit 16 displays the abnormality diagnosis result on the display to notify the operator, or notifies the operator by emitting sound or the like from the speaker.

  Next, the case where the abnormality detection rate in the shade and the solar flare is different from the example in FIG.

(Example 1)
In FIG. 14, the shaded total abnormality detection rate is 0.01, the shade first half event total abnormality detection rate is 0.04, the solar flare total abnormality detection rate is 0.10, and the sun flare first half event total abnormality detection. An example where the rate is 0.10 is shown. In the case of FIG. 14, when the accumulated data collecting unit 22 receives accumulated data up to the time “20:00: 00”, in step S40, the total solar flare abnormality detection rate (0.10) and the first half of the event are totaled. The abnormality detection rate (0.10) is identified as the highest detection rate. In step S42, it is determined whether or not the highest detection rate is the first event total abnormality detection rate. Here, since the total abnormality detection rate is included in the highest detection rate and the determination is negative, the time “20:00: 00” is set as the current abnormality monitoring processing time zone in step S46 ( Diagnosis process (1) in FIG. In step S48, the data analysis unit 242 performs data analysis. Here, when step S46 is passed as described above, the data for the preset time (5 minutes) is analyzed in the direction of going back from the current abnormality monitoring processing time zone “20:00: 00”. Then, perform abnormality monitoring. Thereafter, the same processing as in the above embodiment is repeated. In this case, the abnormality monitoring process is executed while going back in time, such as data from “19:55:00” to 5 minutes before, data from “19:55:00” to 5 minutes before, and so on. Become.

  Thereafter, when the accumulated data collection unit 22 receives data up to the time “23:59:58” at a certain point in time, the time “23: 00: 0” indicated by “diagnosis process (2)” in FIG. It is newly set as the current abnormality monitoring processing time zone (step S46). Then, the data analysis unit 242 reads data from the time “23:00: 00” to 5 minutes before, data from “22:55:00” to 5 minutes ago, and “22:50:00” to 5 minutes ago. Anomaly monitoring processing is executed while going back in time, such as data up to.

(Example 2)
Next, the example of FIG. 15 will be described. In FIG. 15, the shaded total abnormality detection rate is 0.10, the shaded first half event total abnormality detection rate is 0.04, the solar flare total abnormality detection rate is 0.02, and the solar flare first half event total abnormality detection. An example where the rate is 0.02 is shown. In the case of FIG. 15, when the accumulated data collection unit 22 receives the accumulated data up to the time “20:00: 00”, in step S40, the shaded total abnormality detection rate (0.10) is the highest detection rate. And the process proceeds to step S42. In step S42, it is determined whether or not the highest detection rate is only the first half event total abnormality detection rate. Since the determination here is negative, in step S46, the time “20:00: 00” is set as the current abnormality monitoring processing time zone (diagnosis processing (1) in FIG. 15). And after performing the abnormality monitoring process using the data from the time “20:00: 00” to 5 minutes before, as in the different example 1, the data from “19: 55:00” to 5 minutes before, The abnormality monitoring process is executed while going back in time, such as “19:50:00” to data from 5 minutes before.

  Thereafter, when the accumulated data collection unit 22 receives data up to the time “23:59:58”, in step S40, the shaded total abnormality detection rate (0.10) is set as the current abnormality monitoring processing target, Control goes to step S42. In step S42, it is determined whether or not the highest detection rate is only the first half event total abnormality detection rate. Since the determination here is negative, in step S46, the time “23:59:58” is set as the current abnormality monitoring processing time zone (diagnosis processing (2) in FIG. 15). Then, the data analysis unit 242 performs an anomaly while going back in time, such as data from the time “23:59:58” to 5 minutes before and data from the time “23:54:58” to 5 minutes before. Perform monitoring processing.

  In step S32 of the flowchart of FIG. 6, if there is no event that has detected a past abnormality and the determination is negative, the process proceeds to step S36. In step S36, the order determination unit 246 of the accumulated data abnormality monitoring unit 24 sets a time zone close to the current time (in the case of FIG. 8, “20:00:00”) as the current abnormality monitoring processing time zone. Then, the process proceeds to step S48.

  As described above in detail, according to the present embodiment, the accumulated data collecting unit 22 acquires accumulated data accumulated in the artificial satellite 200, and the event collecting unit 30 is collecting the accumulated data. Get information about the event that occurred. Then, the abnormality detection rate calculation unit 244 of the accumulated data abnormality monitoring unit 24 calculates the abnormality detection rate in the artificial satellite 200 when an event has occurred in the past, and the order determination unit 246 determines whether the abnormality has occurred. The analysis order of the accumulated data is determined based on the detection rate, and the data analysis unit 242 analyzes the accumulated data according to the order. Thereby, it is possible to efficiently perform the abnormality analysis of the artificial satellite based on the abnormality detection rate. In particular, in this embodiment, since the data analysis unit 242 preferentially analyzes data collected when a type of event with a high abnormality detection rate occurs, statistical data such as abnormality occurrence characteristics is used. Anomaly analysis can be performed in the priority order required from simple data.

  Moreover, in this embodiment, the data analysis part 242 analyzes the accumulation | storage data collected when the same kind of event has generate | occur | produced in order from the late collection time in principle. As a result, when accumulated data collected when a certain event occurs includes data indicating an abnormality, it is possible to quickly detect the data indicating the abnormality, and thus quickly detect the abnormality. Can be done.

  In this embodiment, the abnormality detection rate calculation unit 244 calculates the abnormality detection rate for the entire event and the abnormality detection rate for the first half of the event for each event type, and the data analysis unit 242 calculates the calculated abnormality detection. If the highest value of the rate is the anomaly detection rate in the first half of the event, analyze the data collected when the most recent event occurred in that event in order from the earliest collection time . Therefore, when there is an abnormality that is likely to occur immediately after the occurrence of a specific event, it is possible to quickly detect the abnormality. For example, the artificial satellite 200 generates electricity using a solar cell owned by itself, stores the electric power in a battery, and operates in the shade when it enters the shade. For this reason, it is considered that abnormalities related to switching between solar cells and batteries are often detected immediately after entering the shade. In this embodiment, when there is a high possibility that there is an abnormality concentrated in the first half of the event, diagnosis is performed from the first half of the event first, so that abnormality detection can be performed quickly and efficiently. is there. The artificial satellite 200 may have a self-recovery function that automatically recovers from an abnormality even if an abnormality occurs. In this way, even if an abnormality occurs in the first half of the event and the self-recovery function is activated and no abnormality is detected at the end of the event, according to the present embodiment, the abnormality recovery can be followed in time series. It is possible to accurately detect the abnormality.

  In the above-described embodiment, the case has been described in which the abnormality detection rates for the entire event and the first half of the event are calculated, and the analysis order of the accumulated data is determined based on them. However, the present invention is not limited to this. For example, only the abnormality detection rate of the entire event may be calculated, and the analysis order of the accumulated data may be determined based only on this. Further, in addition to the entire event and the first half of the event, the abnormality detection rate in the second half of the event may be calculated, and the analysis order of the accumulated data may be determined based on them. Note that the phrase “first half of the event” includes not only the concept of the first half (1/2) of the entire event, but also the concept of a predetermined time period after the occurrence of the event.

  Note that the processing function of the abnormality monitoring system of the above embodiment can be realized by a computer. In that case, a program describing the processing contents of the functions that the abnormality monitoring system should have is installed in the computer, and the processing functions are realized on the computer by executing the program on the computer. The program describing the processing contents can be recorded on a computer-readable recording medium.

  When distributing the program, for example, portable recording media such as a DVD (Digital Versatile Disc) and a CD-ROM (Compact Disc Read Only Memory) on which the program is recorded are sold. It is also possible to store the program in a storage device of a server computer and transfer the program from the server computer to another computer via a network.

  The computer that executes the program stores, for example, the program recorded on the portable recording medium or the program transferred from the server computer in its own storage device. Then, the computer reads the program from its own storage device and executes processing according to the program. The computer can also read the program directly from the portable recording medium and execute processing according to the program. Further, each time the program is transferred from the server computer, the computer can sequentially execute processing according to the received program.

  The above-described embodiment is an example of a preferred embodiment of the present invention. However, the present invention is not limited to this, and various modifications can be made without departing from the scope of the present invention.

22 Accumulated data collection unit (data acquisition means)
30 Event collection part (event information acquisition means)
200 Artificial Satellite 244 Anomaly Detection Rate Calculation Unit (Anomaly Detection Rate Calculation Means)
246 Order determining unit (order determining means)

Claims (6)

Data acquisition means for acquiring collectively the collected data collected in the artificial satellite during communication with the artificial satellite when communication with the artificial satellite can be performed ;
Event information acquisition means for acquiring information on events that occurred while the collected data was being collected;
An anomaly detection rate calculating means for calculating an anomaly detection rate in the artificial satellite when the event has occurred in the past, for each type of the event;
A data analysis order determination apparatus comprising: order determination means for determining an analysis order of the collected data based on the abnormality detection rate. When there are multiple types of events that occur while the collected data is being collected,
2. The order determination unit according to claim 1, wherein the order determination unit determines an analysis order so that data collected when an event of a type with a high abnormality detection rate occurs is preferentially analyzed. Data analysis order determination device.   The order determination means analyzes the data collected when the same type of event occurs so that the data collected when the event that occurred later in time occurs is analyzed in order from the data collected. The data analysis order determination apparatus according to claim 2, wherein the order is determined. The abnormality detection rate calculating means calculates an abnormality detection rate in the entire event and an abnormality detection rate in the first half of the event for each type of the event,
When the calculated abnormality detection rate in the first half is high, the order determination unit determines the analysis order so that data collected when the event occurs is analyzed in order from the earliest collection time. The data analysis order determination device according to claim 3. Computer
Data acquisition means for collectively acquiring the collected data collected in the artificial satellite during communication with the artificial satellite when communication with the artificial satellite becomes possible
Event information acquisition means for acquiring information on events that occurred while the collected data was being collected;
An anomaly detection rate calculating means for calculating an anomaly detection rate in the artificial satellite when the event has occurred in the past for each type of the event, and determining an analysis order of the collected data based on the anomaly detection rate A data analysis order determination program which functions as an order determination means. The abnormality detection rate calculating means calculates an abnormality detection rate in the entire event and an abnormality detection rate in the first half of the event for each type of the event,
When the calculated abnormality detection rate in the first half is high, the order determination unit determines the analysis order so that data collected when the event occurs is analyzed in order from the earliest collection time. The data analysis order determination program according to claim 5, wherein:

Images