JP2020035297A - Apparatus state monitor and program - Google Patents

Abstract

To select an appropriate malfunction determination method according to a state of an apparatus.SOLUTION: An apparatus state monitor 10 includes: a malfunction determination section 11 which determines presence/absence of a malfunction in a building server using determination indexes 17 different from each other; a selection rule information storage section 16 which stores selection rule information established by the malfunction determination section 11 used for determination of the presence/absence of a malfunction when executing the processing of the building server corresponding to a type of processing executed in the building server; a determination method selection section 12 which selects the malfunction determination section 11 used for determination of the presence/absence of a malfunction when executing processing of the building server 5 from a plurality of malfunction determination sections 11 prepared for with reference to the selection rule information; an execution control section 13 which makes the selected determination method (malfunction determination section 11) execute malfunction determination; and a display control section 14 which controls a display of a determination result.SELECTED DRAWING: Figure 2

Description

  The present invention relates to an apparatus status monitoring apparatus and a program, and more particularly, to determination of presence or absence of an abnormality in an apparatus used for monitoring equipment installed in a facility.

  In the field of building systems such as BAS (Building Automation System) and BMS (Building Management System), services that optimally control equipment based on the results of analyzing equipment and sensor data, such as demand response and ZEB (Zero Energy) (Building). In providing these services, it is important to establish a technique for monitoring the state of the equipment that makes up the building system.

  When monitoring a device, various technologies have been proposed that monitor the state of the device and determine whether an abnormality has occurred in the device.

  For example, the status of the device indicated by the resource load of the device (CPU usage, memory usage, I / O access number, etc.) is monitored, and the device is determined to be abnormal based on the value indicated by the resource load. Alternatively, the device is determined to be abnormal when the time from when the request is transmitted to the device to when the response is received (response time) exceeds the threshold value. Alternatively, focusing on the fact that there is a correlation between the resource load of the CPU and the number of requests (the number of input packets), a regression line indicating the correlation is calculated, and the deviation of the relationship between the latest load and the number of input packets from the regression line is calculated. An abnormality of the device is determined according to the degree.

  Furthermore, after determining that there is an abnormality due to the resource load, the validity of the determination is verified by referring to the state of the hardware. Alternatively, when the operation of the device is controlled according to a schedule, a resource load model for operating according to the schedule is prepared in advance, and the abnormality of the device is determined based on the degree of deviation from the load model.

  As described above, various types of abnormality determination techniques that refer to various indices have conventionally been proposed.

JP-A-06-201415 JP-A-2000-056823 JP 2017-187820 A JP 2011-070635 A

  Each of the abnormality determination methods described above has a possibility of erroneous determination. For example, in the method of monitoring only the response time, there is a possibility that an abnormality is erroneously determined due to a change in the response time that occurs when requests for devices are concentrated. In order to solve this, it has been proposed to use the correlation between the load of the CPUP and the number of input packets as an index for determination. However, a period in which there is a correlation between the load and the number of packets and a period in which the correlation does not exist ( For example, in a situation where monitoring processes and data compression processes that are not related to an external request are mixed, an erroneous determination may occur.

  As described above, each of the conventionally proposed abnormality determination methods can correctly perform an abnormality determination under a predetermined condition, but can perform an abnormality under a situation where the environment of a building system or the state of equipment changes. It has been difficult to always make a determination with high accuracy.

  An object of the present invention is to make it possible to select an appropriate abnormality determination method according to the state of a device.

  The device state monitoring device according to the present invention is implemented by a plurality of abnormality determination units that determine presence / absence of abnormality in a device used for monitoring equipment installed in a facility, using different determination indices, and executed by the device. A selection rule information storage unit that stores selection rule information in which the abnormality determination unit used to determine the presence or absence of an abnormality when the device performs the process in accordance with the type of the processing to be performed; Selecting means for selecting the abnormality determining means used for determining the presence or absence of an abnormality during execution of the processing of the device from the plurality of abnormality determining means with reference to information.

  The apparatus further includes means for acquiring log information generated along with execution of a process in the device, wherein the selection unit is used to determine whether there is an abnormality in the device when executing the process specified by the log information. This is for selecting the abnormality determination means.

  The apparatus further includes: a unit configured to acquire load information regarding a load when a process is performed in the device; and a process prediction unit configured to predict a process performed by the device based on the load information. And selecting the abnormality judging means used for judging the presence or absence of an abnormality in the device at the time of executing the processing predicted by the processing predicting means.

  A means for acquiring log information generated in association with execution of a process in the device; a means for acquiring load information relating to a load when the process is executed in the device; and analyzing the load information and the log information. And a rule setting means for setting the selection rule information.

  Further, when there are a plurality of the devices, the selection unit refers to information related to other devices together when selecting the abnormality determination unit for a device to be determined whether there is an abnormality. is there.

  The virtual machine further includes a rule setting unit that sets the selection rule information by analyzing load information and log information obtained by causing a virtual device to execute a process on the virtual system.

  The apparatus further comprises means for acquiring schedule information including an execution schedule of a process in the device, wherein the selecting unit is configured to determine whether or not the device is abnormal when executing the process scheduled in the schedule information. This is for selecting the judging means.

  The program according to the present invention can execute a plurality of abnormality determination units that determine presence or absence of an abnormality in a device used for monitoring equipment installed in a facility by using different determination indices, and can be executed by the device. In response to the type of processing to be performed, the abnormality determination means used to determine the presence or absence of an abnormality at the time of execution of the processing of the device can access selection rule information storage means for storing selection rule information set therein. For causing a computer to function as a selection unit that selects the abnormality determination unit used to determine the presence or absence of an abnormality during execution of the processing of the device from the plurality of abnormality determination units with reference to the selection rule information. It is.

  According to the present invention, it is possible to select an appropriate abnormality determination method according to the state of the device.

1 is an overall configuration diagram of a building system including an embodiment of a device state monitoring device according to the present invention. FIG. 2 is a block configuration diagram of the device state monitoring device according to the first embodiment. 5 is a diagram illustrating an example of a data configuration of log information recorded in a log information storage unit according to Embodiment 1. FIG. FIG. 4 is a diagram illustrating a data configuration example of selection rule information set in a selection rule information storage unit according to the first embodiment. 5 is a flowchart illustrating an abnormality determination process according to the first embodiment. 5 is a flowchart illustrating a selection process performed by a determination method selection unit according to the first embodiment. 5 is a flowchart illustrating an abnormality determination process performed by an abnormality determination unit according to the first embodiment. 5 is a flowchart illustrating an abnormality determination process performed by another abnormality determination unit in the first embodiment. 5 is a flowchart illustrating an abnormality determination process performed by another abnormality determination unit in the first embodiment. FIG. 9 is a block configuration diagram of a device state monitoring device according to a second embodiment. FIG. 13 is a block configuration diagram of a device state monitoring device according to a third embodiment.

  Hereinafter, a preferred embodiment of the present invention will be described with reference to the drawings.

Embodiment 1 FIG.
FIG. 1 is an overall configuration diagram of a building system including an embodiment of a device state monitoring device according to the present invention. FIG. 1 shows a configuration of a building system in which a system installed in a building 1 and a monitoring center 2 are connected via a network 3. The monitoring center 2 is a center for remotely monitoring facilities 4 installed in the building 1. The system on the building 1 side is configured by connecting a building server 5, a controller 6, a monitoring terminal 7, and a device state monitoring device 10 to a network 8. The building server 5 collects, via the controller 6, data representing the state of the facility 4 and information about the facility 4 such as set values for the facility 4. Information on the facility 4 may be collected from various sensors (not shown) installed around the facility 4 as well as the facility 4. The controller 6 controls the operation of the equipment 4, collects various information related to the equipment 4, and sends the collected information to the building server 5. It should be noted that the numbers of the controllers 6 and the facilities 4 shown in FIG. 1 are merely examples, and the present invention is not limited thereto. The monitoring terminal 7 is an information processing device used by a building manager or the like, and is realized by, for example, a personal computer (PC).

  The device status monitoring device 10 is a device used to monitor the status of devices included in the system on the building 1 side. The devices to be monitored by the device status monitoring device 10 are devices used for monitoring the facilities 4, and in the case of the present embodiment, correspond to the building server 5 and the controller 6. The device state monitoring device 10 can be implemented by a computer such as a PC that has been used in the past. That is, the device state monitoring apparatus 10 includes communication means such as a storage means such as a CPU, a ROM, a RAM, and a hard disk drive (HDD), a user interface including an input means such as a mouse and a keyboard, and a display means such as a display, and a network interface. Have.

  FIG. 2 is a block diagram of the device status monitoring apparatus 10 according to the present embodiment. The device state monitoring device 10 according to the present embodiment includes an abnormality determination unit 11, a determination method selection unit 12, an execution control unit 13, a display control unit 14, a log information storage unit 15, and a selection rule information storage unit 16. . Note that components not used in the description of the present embodiment are omitted from the drawings.

  In the present embodiment, a plurality of abnormality determination units 11 are provided. Each abnormality determination unit 11 determines the presence or absence of an abnormality in the device using a different determination index. That is, the presence or absence of an abnormality in the device is determined by different determination methods. The judgment method itself may use a judgment method that has existed before. The term “different judgment index” in the present embodiment means that completely the same index is not used, and this also applies to the case where the same index is partially used or the index used is inclusive. I do. Specifically, when the determination method 1 uses the indices A, B, and C and the determination method 2 uses the indices A, B, and D, the determination method 1 and the determination method 2 use the common indices A and B. Since they are not completely the same, different judgment indices will be used. When the determination method 1 uses the indices A, B, and C, and the determination method 2 uses the indices A, B, C, and D, the determination method 2 uses the index D that is not used by the determination method 1. is not. Therefore, the determination method 1 and the determination method 2 use different determination indices. Further, the “abnormality” in the present embodiment is not limited to a failure in which the device stops, but also includes a failure in which the device does not stop and a warning-level abnormality that has not been exposed. As described above, the devices to be subjected to the abnormality determination are the building server 5 and the controller 6, but in the following description, the building server 5 is assumed to be the device unless otherwise specified. In the present embodiment, a conventional determination method is used effectively, and one of the determination methods is selectively and effectively used.

  The determination method selection unit 12 refers to the selection rule information set in the selection rule information storage unit 16 and determines whether there is an abnormality during execution of the processing of the building server 5 from the plurality of abnormality determination units 11 prepared. The abnormality determination unit 11 used for the determination is selected. The execution control unit 13 controls the execution of the abnormality determination unit 11 selected by the determination method selection unit 12. The display control unit 14 controls display of an execution result of the abnormality determination unit 11. The log information accumulating unit 15 accumulates log information generated in association with execution of processing in the devices (the building server 5 and the controller 6) included in the building system. For example, the log information corresponding to the building server 5 includes log information of a process of transmitting data in response to a request from the monitoring center 2, a tallying process of facility data held by the building server 5, and a 6 includes log information and the like of a process of collecting data from.

  FIG. 3 is a diagram illustrating an example of log information recorded in the log information storage unit 15 according to the present embodiment. The log information is generated as the processing is executed in the device, and is registered in the log information storage unit 15. The log information records the date and time of generation of the log information, information for specifying the type of executed processing (hereinafter, also referred to as “type identification information”), and the like. In the description example of the log information illustrated in FIG. 4, the character string following “Log:” corresponds to the type identification information.

  FIG. 4 is a diagram illustrating a data configuration example of the selection rule information set in the selection rule information storage unit 16 according to the present embodiment. In the selection rule information, an abnormality determination unit 11 used for determining whether or not there is an abnormality at the time of execution of the processing of the building server 5 is set in accordance with the type of the processing executed in the building server 5. Specifically, a condition (determination method setting rule) for specifying the abnormality determination unit 11 and identification information (determination method ID) of the abnormality determination unit 11 correspond to a rule ID for identifying each selection rule. Is set. The type identification information set in the log information is set as the condition set in the selection rule information. “Respond_to_cloud” and “aggregate_daily” in FIG. 4 correspond to the type identification information. Although FIG. 4 illustrates selection rules corresponding to five types of processes, the types and number of processes are not limited to these.

  As described above, each abnormality determination unit 11 determines the presence or absence of an abnormality when the processing is executed in the building server 5 using different determination indices. FIG. 2 illustrates the hardware state of the building server 5, the response time of the CPU, the load model, the correlation information, and the load information as examples of the determination index 17. The hardware status is information indicating the status of hardware such as the frequency of the CPU. The response time is the time from transmitting a request to a device to receiving a response. The load model is a resource load model when the device operates according to the control schedule. The correlation information is information indicating a correlation between the resource load of the CPU and the number of requests (the number of input packets). The load information is information on a load on the device. In the present embodiment, the load includes hardware resources such as a CPU usage rate, a memory usage amount, and the number of I / O accesses, and a network load such as the number of transmitted and received bytes and the number of packet transfers per time. The device state monitoring device 10 can acquire the information at each point in the past and the information at the present time as the determination index 17 from the device that manages the information, and may hold the information internally if necessary. These determination methods (abnormality determination unit 11) and determination indices are merely examples, and need not be limited to these.

Note that, in FIG. 2, the correspondence between the determination index 17 and the abnormality determination unit 11 is indicated by an arrow, but in FIG. 2, it is schematically illustrated that each abnormality determination unit 11 uses a different determination index. It is just that. That is, this does not mean that the abnormality determination unit 11 with ID = 001 uses only the index of the hardware state, and the abnormality determination unit 11 with ID = 002 uses only the index of the response time. In some cases, a plurality of determination indices are referred to from among the determination indices 17 as illustrated in the abnormality determination unit 11 of FIG. Further, a certain judgment index 17 may be obtained by referring to another judgment index 17. For example, the load model is a determination index obtained based on the load information.

  Each component 11 to 14 of the device status monitoring device 10 is realized by a cooperative operation of a computer forming the device status monitoring device 10 and a program that runs on a CPU mounted on the computer. Each of the storage units 15 and 16 is realized by an HDD mounted on the device state monitoring device 10. Alternatively, a RAM or an external storage means may be used via a network.

  In addition, the program used in the present embodiment can be provided not only by communication means but also stored in a computer-readable recording medium such as a CD-ROM or a USB memory. The program provided from the communication means or the recording medium is installed in the computer, and various processes are realized by the CPU of the computer sequentially executing the program.

  When monitoring the facility 4, the building server 5 executes various processes related to facility monitoring. The processing to be executed includes an aggregation processing using a relatively large number of CPUs and a communication processing with the monitoring center 2 and the controller 6 not using a relatively large number of CPUs. As described above, there are various types of processing to be executed, such as processing that uses a relatively large amount of CPU, processing that uses a relatively large amount of RAM, and processing that mainly performs network communication. When the building server 5 is executing a process that uses a lot of CPU, it is preferable to use the abnormality determining unit 11 that uses the resource load and the response time of the CPU as the determination index 17 to determine the abnormality of the building server 5 at that time. It is. When the building server 5 is executing a process mainly for data communication, it is preferable to use the abnormality determining unit 11 that uses the communication load as the determination index 17 to determine the abnormality of the building server 5 at that time. is there. When a process mainly involving data communication is being executed, it is not rare that a relatively large number of CPUs are used, so that an abnormality is determined without referring to the data communication determination index 17 called the CPU response time. Is not always accurate. There is no problem if there is an abnormality determination method that can cope with all situations, but it is hard to say that an abnormality determination method suitable for all situations can be created at present as a variety of functions continue to be provided. On the other hand, as described above, there is an abnormality determination method that can accurately determine an abnormality under a specific situation.

  What is characteristic in the present embodiment is that the abnormality determining unit 11 used for determining the presence or absence of an abnormality in the building server 5 can be selected according to the type of processing executed in the building server 5. Accordingly, the abnormality determination unit 11 that refers to the determination index 17 that is appropriate for the type of processing can be executed to perform the abnormality determination, so that the abnormality determination of the device can be performed with high accuracy.

  Hereinafter, the abnormality determination processing according to the present embodiment will be described with reference to the flowchart shown in FIG.

  The administrator who executes the abnormality determination designates log information corresponding to the process to be determined from the log information stored in the log information storage unit 15. In response, the determination method selection unit 12 extracts the type of processing recorded in the designated log information (Step 102). Subsequently, the determination method selection unit 12 refers to the selection rule information, and specifies a determination method used for abnormality determination based on the determination method ID corresponding to the type of the extracted process, that is, the abnormality determination unit 11 (Step 103). .

  FIG. 6 is a flowchart illustrating the determination method specifying process (step 103) in the determination method selection unit 12 based on the selection rule information illustrated in FIG. As shown in FIG. 6, the determination method selection unit 12 compares the type of the process extracted from the log information with each type identification information set in the selection rule information (Step 1031). Then, if the type of processing extracted from the log information matches “response_to_cloud” (Y in step 1032), the determination method selection unit 12 specifies the determination method provided by the abnormality determination unit 11 with ID = 005 (step 1033). Alternatively, if the processing type matches “collect_controller_data” (Y in step 1034), the determination method selection unit 12 specifies the determination method provided by the abnormality determination unit 11 with ID = 001 (step 1033). If the type of the processing is other than the above, specifically, if it matches any of “aggregate_daily”, “aggregate_monyhly”, or “aggregate_yearly” (N in step 1034), the determination method selection unit 12 sets the ID = 004. The determination method provided by the abnormality determination unit 11 is specified (step 1036).

  When the abnormality determination unit 11 is specified in this way, the execution control unit 13 activates the corresponding abnormality determination unit 11 to execute the abnormality determination (Step 104). The activated abnormality determination unit 11 acquires the determination index 17 at a necessary time (period) by referring to the log information, and performs abnormality determination when the building server 5 executes the process. In the present embodiment, the normal / abnormal judgment processing executed by the abnormality judgment unit 11 effectively utilizes the existing judgment processing. Hereinafter, a typical judgment processing will be briefly described.

  FIG. 7 is a flowchart illustrating an example of a normal / abnormal determination process performed by the abnormality determination unit 11 according to the present embodiment. 4 is a flowchart specifically showing processing in step 104. When acquiring the load information (step 411), the abnormality determination unit 11 that executes the abnormality determination process illustrated in FIG. 7 calculates a response time from transmitting a request to the device to receiving a response (step 412). If the response time is equal to or less than the predetermined threshold value (Y in step 413), it is determined that the response is promptly possible and that the response is normal (step 417). If the response time exceeds the predetermined threshold (N in step 413), a correlation between the resource load of the CPU and the number of requests (the number of input packets) is calculated (step 414). If the correlation does not satisfy the predetermined threshold (N in step 415), it is finally determined that the state of the device is normal (step 417). On the other hand, if the correlation is equal to or greater than the predetermined threshold (Y in step 415), the state of the device is determined to be abnormal (step 416). An appropriate value is set for each of the thresholds based on the performance information.

  FIG. 8 is a flowchart illustrating an example of a normal / abnormal determination process performed by the abnormality determination unit 11 that is different from the abnormality determination unit 11 that performs the abnormality determination process illustrated in FIG. When acquiring the load information (step 421), the abnormality determination unit 11 executing the abnormality determination process illustrated in FIG. 8 calculates data indicating a resource load to be compared with the load model (step 422). Then, when a load model is obtained (step 422). The state of the device is determined by comparing the resource load calculated from the load information with the load model. If the device can be determined to be normal (Y in step 424), the device status is determined to be normal (step 429). On the other hand, when the device cannot be determined to be normal (N in step 424), the abnormality determination unit 11 subsequently acquires hardware information (step 415). Then, after the acquired load information is corrected as necessary such as normalization (step 426), the state of the device is determined by comparing with the hardware information. Here, when the device can be determined to be normal (Y in step 427), the device status is finally determined to be normal in this processing (step 429). On the other hand, if the device cannot be determined to be normal by this determination process (N in step 427), the device status is finally determined to be abnormal in this process (step 428).

  FIG. 9 is a flowchart illustrating an example of a normal / abnormal determination process performed by the abnormality determination unit 11 different from the abnormality determination unit 11 that performs the abnormality determination process illustrated in FIGS. 7 and 8. The abnormality determination unit 11 that executes the abnormality determination processing illustrated in FIG. 9 performs the processing (steps 431 to 434) from the acquisition of the load information to the determination of the state of the device in the determination processing (steps 421 to 421) illustrated in FIG. 424), and a description thereof will not be repeated. If the device cannot be determined to be normal (N in step 434), the load information is corrected to eliminate disturbance (step 435), and the state of the device is determined again. Here, when the device can be determined to be normal (Y in step 436), the device status is finally determined to be normal in this processing (step 440). On the other hand, if the device cannot be determined to be normal (N in step 436), the time is shifted with respect to the load information (step 437), and the state of the device is determined again. If the device can be determined to be normal (Y in step 438), the device status is finally determined to be normal in this processing (step 440). On the other hand, if the device cannot be determined to be normal by the determination process (N in step 438), the status of the device is finally determined to be abnormal in this process (step 428).

  As in the determination process performed by each of the abnormality determination units 11 described above, each abnormality determination unit 11 determines the state of the device by combining a plurality of determination indices or correcting load information as necessary.

  When the abnormality determination unit 11 performs the abnormality determination, the display control unit 14 notifies the administrator by displaying the processing result (the abnormality determination result) on a display or the like (Step 105). Alternatively, the abnormality determination result may be transmitted to the monitoring terminal 7 and displayed. The processing result may be stored in a predetermined storage means so that it can be referred to later.

  According to the present embodiment, it is possible to select the determination method (the abnormality determination unit 11) used for determining the abnormality of the device according to the type of processing included in the log information. This makes it possible to further improve the accuracy of the abnormality determination.

  In the present embodiment, the components necessary for abnormality determination are collected in the device status monitoring device 10, but the components (processing functions) of the device status monitoring device 10 are stored in the building server 5, the monitoring terminal 7, or the monitoring device. The center 2 may have it. Alternatively, the processing function may be distributed to a plurality of locations, and the apparatuses may be operated in cooperation with each other to execute the above-described abnormality determination processing.

Embodiment 2 FIG.
In the first embodiment, the abnormality determination at the time of execution of the processing executed in the past in the building server 5 is performed by referring to the log information. In the present embodiment, a process before log information is recorded in the log information accumulating unit 15, that is, a process that is currently being executed or is to be executed in the future is predicted, and an abnormality can be determined at the time of executing the process. It is characterized by the following.

  FIG. 10 is a block configuration diagram of the device state monitoring device 10 according to the present embodiment. The overall configuration of the building system and the hardware configuration report of the device status monitoring device 10 according to the present embodiment may be the same as those of the first embodiment. The device status monitoring device 10 according to the present embodiment has the configuration according to the first embodiment. And a processing prediction unit 18 is added. The same components as those in the first embodiment are denoted by the same reference numerals, and description thereof will be omitted. The process prediction unit 18 predicts a process to be executed by the device based on the load information. The processing prediction unit 18 is realized by a cooperative operation of a computer forming the device state monitoring device 10 and a program running on a CPU mounted on the computer.

  Next, an abnormality determination process according to the present embodiment will be described. The basic processing flow of the present embodiment may be the same as that of the first embodiment. In the first embodiment, the processing to be determined as an abnormality is specified by referring to the log information in step 101. This embodiment is different from the first embodiment in a method for specifying a process to be subjected to the abnormality determination.

  That is, the process prediction unit 18 predicts a process being executed in the building server 5 or a process to be executed in the future without referring to the log information by referring to the load information. For example, when the load information is analyzed, it may be appropriate to perform the abnormality determination using the abnormality determination unit 11 having the determination method ID of 005 because the communication load is currently high. If the rate is predicted to increase and will tend to increase in the future, the processing prediction unit 18 starts executing processing that uses more CPU, in addition to processing with a high communication load, for example, aggregation processing. Predict that. As described above, the processing may be executed in parallel. Note that log information relating to the process being executed or to be executed is not generated.

  As described above, when the processing that is being executed or is to be executed is predicted by the analysis of the load information by the processing prediction unit 18, the determination method selection unit 12 refers to the selection rule information and determines the type of the predicted processing ( For example, the determination method corresponding to “aggregate_daily”), that is, the abnormality determination unit 11 whose determination method ID is 003 is specified. Subsequent processes performed by the execution control unit 13, the abnormality determination unit 11, and the display control unit 14 may be the same as those in the first embodiment, and a description thereof will not be repeated.

  Here, it has been described that the determination method selection unit 12 specifies the determination method with reference to the analysis result of the processing load by the processing prediction unit 18. However, the analysis result of the processing load by the processing prediction unit 18 is displayed on the screen. It may be displayed to allow the administrator to select the type of processing.

  Further, in the present embodiment, even if the communication process is being executed at the present time, the determination method is switched by giving priority to improving the accuracy of the abnormality determination of the counting process that has been started later. The determination method may be switched after waiting.

  In the first embodiment, the abnormality determination of the process executed in the past is performed with reference to the past log information. However, according to the present embodiment, the determination used for the abnormality determination of the current or the process to be performed is performed. The method (abnormality determination unit 11) can be selected.

Embodiment 3 FIG.
In the first embodiment, the determination method is specified by referring to the selection rule information generated in advance by the administrator or the like. However, the present embodiment has a function of generating by learning the selection rule. It is characterized by having.

  FIG. 11 is a block configuration diagram of the device state monitoring device 10 according to the present embodiment. The overall configuration of the building system and the hardware configuration report of the device status monitoring device 10 according to the present embodiment may be the same as those of the first embodiment. The device status monitoring device 10 according to the present embodiment has the configuration according to the first embodiment. And a rule learning unit 19 is additionally provided. The same components as those in the first embodiment are denoted by the same reference numerals, and description thereof will be omitted. The rule learning unit 19 is provided as a rule setting unit, and sets selection rule information by analyzing load information and log information. The rule learning unit 19 is realized by a cooperative operation of a computer forming the device state monitoring device 10 and a program running on a CPU mounted on the computer.

  Next, an abnormality determination process according to the present embodiment will be described. The basic processing flow of the present embodiment may be the same as that of the first embodiment. In the first embodiment, in step 103, selection rule information used for specifying a determination method is generated as follows.

  That is, the rule learning unit 19 divides the load information into a certain period. Then, for each divided period, the process executed in the divided period is extracted by analyzing the log information corresponding to the divided period. For example, the load information is divided every hour. Then, the processing executed within the one hour is extracted. Although the processing may be executed concurrently, here, the processing including the processing executed concurrently is extracted. Here, assuming that about one, two, or three types of processing are extracted, the rule learning unit 19 sets processing identification information corresponding to the type of the extracted processing as a condition in the selection rule information. FIG. 4 shows an example in which only one process identification information is set for each selection rule information. However, in this embodiment, a plurality of process types may be associated with one selection rule. . In the above description, the time length for dividing the load information into a certain period is one hour. However, in practice, it is preferable to divide the load information into a time length in which about one, two, or three types of processing can be extracted. is there.

  When the conditions to be set in the selection rule (about one, two, or three types of processing) are determined as described above, the rule learning unit 19 associates the determination method with this condition. This is, for example, by referring to the selection rule information in which the process identification information of only one process is set in the condition, and determining the abnormality of the process whose execution number is the largest in the division period among the types of processes set in the condition. May be associated with the determination method associated with. Alternatively, the type of processing set in the condition may be displayed on a screen, and the administrator may specify a determination method.

  In the selection rule generation method according to the present embodiment, there is a possibility that a selection rule in which the combination of processes set in the condition is the same and different determination methods are associated is generated. May be selected by an administrator or the like.

Embodiment 4 FIG.
In the first and third embodiments, the building server 5 has been described as an example of a device, and description has been made on the assumption that log information for the building server 5 is referred to. However, since the building system is characterized by a hierarchical type and a decentralized autonomous type, there is a possibility that the state of a specific device cannot be completely recognized only by referring to the state (log information) of the specific device. For example, a failure may occur in a specific device (the building server 5) and log information may not be generated.

  Therefore, in the present embodiment, the state of the entire pill system is monitored from load information and log information of a plurality of devices. Specifically, when determining whether there is an abnormality when the processing is executed in the building server 5, the controller 6 and the monitoring center 2 communicate with the building server 5 and execute the processing in cooperation with each other. Since the case can be assumed, the determination method selection unit 12 refers to not only the building server 5 but also the log information of other devices such as the controller 6 and the monitoring center 2 together. In this way, the accuracy of the selection of the determination method used for the abnormality determination when the processing is executed in the building server 5 is improved.

Embodiment 5 FIG.
The present embodiment is characterized in that selection rule information is generated using a virtual building system.

  The virtual building system is a system in which each device of the real building system is virtualized by a virtual machine, a container, or the like, and is realized on a computer. The virtual building system has rule setting means for setting selection rule information by analyzing load information and log information. The rule setting means analyzes the load information and the log information generated by monitoring the status of each component (the central monitoring function and the controller function) of the virtual building system, and determines the result of each determination method (determination of presence / absence of abnormality) Based on the validity of the result), a determination method suitable for the type of the process is obtained. Then, a selection rule for the determination method is generated.

  The selection rule information generated in this way is applied to the device state monitoring device 10 of the actual building system. This makes it possible to generate a highly accurate selection rule before starting operation on a real device.

Embodiment 6 FIG.
In the first embodiment, the processing executed in the building server 5 is specified with reference to the log information. When the building server 5 executes a process according to a preset control schedule, it is possible to specify a process to be executed in the building server 5 by referring to the control schedule. That is, the device state monitoring device 10 according to the present embodiment acquires the control schedule from the building server 5 or the monitoring center 2 that manages the control schedule. Then, the determination method selection unit 12 refers to the selection rule information and selects a determination method (the abnormality determination unit 11) used to determine whether or not there is an abnormality in the building server 5 at the time of executing the process scheduled in the control schedule.

  According to the present embodiment, it is possible to specify a process by referring to a control schedule instead of log information, and to select a determination method (the abnormality determination unit 11) used for determining an abnormality when the process is performed.

  The selection method of the determination method and the generation of the selection rule described in each of the above embodiments may be executed in an appropriate combination.

Embodiment 7 FIG.
In each of the above-described embodiments, one of the determination methods (abnormality determination unit 11) that is estimated to obtain a highly accurate determination result according to the selection rule is selected, and the determination result in the abnormality determination unit 11 is presented. I did it.

  In the present embodiment, it is possible to obtain a determination result using all the determination methods without narrowing down the determination method used for determining the presence or absence of abnormality to one, and to perform the abnormality determination based on the determination result. It is characterized by:

  That is, simply, the one with the larger number of determination methods determined to be normal and the number of determination methods determined to be abnormal is determined as the correct answer for abnormality determination. An abnormality is determined by a so-called majority decision. In this case, the selection rule becomes unnecessary. In addition, in the case of majority decision, it is equivalent to adding one determination method as 1, but the determination method may be weighted with reference to the selection rule.

1 building, 2 monitoring centers, 3 and 8 networks, 4 facilities, 5 building servers, 6 controllers, 7 monitoring terminals, 10 equipment status monitoring devices, 11 abnormality determination units, 12 determination method selection units, 13 execution control units, 14 display Control section, 15 log information storage section, 16 selection rule information storage section, 17 judgment index, 18 processing prediction section, 19 rule learning section.

Claims (8)

Using a plurality of different determination indicators, a plurality of abnormality determination means for determining the presence or absence of an abnormality in the equipment used to monitor the equipment installed in the facility,
Selection rule information storage means for storing selection rule information in which the abnormality determination means used for determining the presence or absence of an abnormality at the time of execution of the processing of the device is set in accordance with the type of processing executed in the device; When,
A selection unit that selects the abnormality determination unit used for determining the presence or absence of an abnormality during execution of the processing of the device from the plurality of abnormality determination units with reference to the selection rule information;
An apparatus status monitoring device comprising: Having means for acquiring log information generated along with execution of processing in the device,
2. The device state monitoring device according to claim 1, wherein the selection unit selects the abnormality determination unit that is used to determine whether there is an abnormality in the device when executing the process specified by the log information. 3. Means for obtaining load information regarding a load when a process is executed in the device;
Processing prediction means for predicting processing to be executed in the device based on the load information,
Has,
2. The apparatus state monitoring apparatus according to claim 1, wherein the selection unit selects the abnormality determination unit used to determine whether there is an abnormality in the device when executing the processing predicted by the processing prediction unit. Means for acquiring log information generated along with execution of processing in the device;
Means for obtaining load information regarding a load when a process is executed in the device;
A rule setting unit that sets the selection rule information by analyzing the load information and the log information;
The device state monitoring device according to claim 1, comprising:   The selecting means, when there are a plurality of the devices, when selecting the abnormality determination means for the device to be determined whether there is an abnormality, and also refers to information associated with other devices together, The device status monitoring device according to claim 1, wherein   The apparatus according to claim 1, further comprising a rule setting unit configured to set the selection rule information by analyzing load information and log information obtained by causing a virtual apparatus to execute a process on the virtual system. Condition monitoring device. Having means for acquiring schedule information including an execution schedule of processing in the device,
The device status monitoring apparatus according to claim 1, wherein the selection unit selects the abnormality determination unit used to determine whether there is an abnormality in the device when executing a process scheduled in the schedule information. Using different determination indices, a plurality of abnormality determination means for determining the presence or absence of an abnormality in the equipment used for monitoring the equipment installed in the facility can be executed, corresponding to the type of processing executed in the equipment A computer that can access a selection rule information storage unit that stores selection rule information in which the abnormality determination unit that is used to determine the presence or absence of an abnormality when the device performs the process,
A program for functioning as a selection unit that selects the abnormality determination unit used for determining the presence or absence of an abnormality during execution of the processing of the device from the plurality of abnormality determination units with reference to the selection rule information.