JP2012093905A - Energy use state analysis system and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an energy use state analysis system capable of categorizing each monitor object device for each system of a distribution device which distributes energy to the monitor object device.SOLUTION: The present invention relates to an energy use state analysis system. And the energy use state analysis system comprises: means for storing distribution state data indicating values of energy amount distributed by a distribution device, in time series, for each distribution device which distributes energy to one or multiple monitor object devices; means for storing operating state data indicating values of an operating state of each of the monitor object devices, in time series; and means for analysing relation between the distribution state data for each distribution device and the operating state data for each monitor object device, and analysing under which distribution device each monitor object device is, by using the analysis result of the relation.

Description

  The present invention relates to an energy usage situation analysis system and program, and can be applied to a building energy management system that analyzes an energy usage situation for each area in a building such as a building.

  Conventionally, the building energy management system (BEMS; Building and Energy Management System) has been used to analyze and grasp the energy usage situation in units of buildings. In BEMS, it is possible to acquire energy usage information in units of buildings such as by area such as floor, by use such as lighting / air conditioning / outlet, and by time zone. As a result, floors and uses / time zones where wasteful energy use is being performed are detected, and demand control is performed during peak hours of power usage. can do.

  On the other hand, since BEMS is a system for the purpose of grasping the energy utilization status in units of buildings, it is difficult to grasp and control the energy utilization status of each monitoring target device. Therefore, Patent Document 1 describes a power management system that allows a user to easily set control by setting power control of a monitoring target device such as an electric device individually with priority. ing.

  In the technique described in Patent Literature 1, when the name, location, ID, and address of a monitoring target device are stored in association with each other, a priority order for suppressing power consumption control is set, and an overcurrent of the main breaker is determined In addition, with reference to the name or location, ID, and address of the monitoring target device, the monitoring target device having a higher priority and name is controlled with priority to avoid overcurrent of the main breaker. Thereby, in the technique described in Patent Document 1, it is possible to provide a power management system that allows a user to easily set power control of a monitoring target device.

JP 2009-130973 A

  As described above, the technology described in Patent Document 1 provides a power management system that allows a user to easily perform control settings by setting power control settings for monitoring target devices individually with priorities. is doing.

  On the other hand, in order to set the priority order to control the power consumption of the monitored devices, the time period during which each monitored device operates and how much energy (electric energy) is used Need to figure out. In addition, when the main breaker is divided into a plurality of systems (distribution devices such as distribution boards) and detailed control is performed for each area such as a floor unit, it is necessary to classify each device to be monitored by system. However, in the technology described in Patent Document 1, it is not possible to classify each system for each monitoring target device, and it is impossible to grasp the details of the energy usage status for each monitoring target device.

  Therefore, an energy utilization state analysis system and program that can classify each monitoring target device for each system of distribution devices that distribute energy to the monitoring target device is desired.

  The energy usage status analysis system of the first aspect of the present invention is (1) for each distribution device that distributes energy to one or a plurality of monitoring target devices, representing a value related to the amount of energy distributed by the distribution device in time series. Distribution status data holding means for holding the distribution status data, (2) operation status data holding means for holding operation status data representing the status related to the operation status of each of the monitoring target devices in time series, ( 3) Analyzing the relationship between the distribution status data for each of the distribution devices held by the distribution status data holding means and the operation status data for each of the monitoring target devices held by the operation status data holding means, Utilizing each of the devices to be monitored has a classifying means for classifying to which of the distribution devices it belongs.

  The energy usage situation analysis program according to the second aspect of the present invention distributes the computer constituting the energy usage situation analysis system for each (1) distribution apparatus that distributes energy to one or a plurality of monitoring target devices. Distribution status data holding means for holding distribution status data representing a value related to the amount of energy in time series; (2) operating status data representing values related to the operating status of each of the monitoring target devices in time series; (3) distribution status data for each distribution device held by the distribution status data holding unit, and operation status data for each monitoring target device held by the operation status data holding unit Which of the distribution devices belong to each of the monitored devices using the analysis result Characterized in that to function as a classifying means for classifying.

  Provided is an energy utilization state analysis system capable of classifying each monitoring target device for each distribution device system that distributes energy to the monitoring target device.

It is the block diagram shown about the connection relation of the various apparatuses relevant to 1st Embodiment. It is the flowchart shown about operation | movement of the analysis system which concerns on 1st Embodiment. It is the graph shown about the time series data accumulate | stored in the analysis system which concerns on 1st Embodiment, and the intermediate data at the time of analyzing the time series data. It is the graph shown about the intermediate data at the time of analyzing time series data in the analysis part which concerns on 1st Embodiment. It is explanatory drawing shown about the structure of the matrix for calculating | requiring the correlation of time series data in the analysis part which concerns on 1st Embodiment. It is explanatory drawing shown about the process which analyzes the combination of the time series data correlated using a matrix in the analysis part which concerns on 1st Embodiment. It is the block diagram shown about the connection relation of the various apparatuses related to 2nd Embodiment. It is the flowchart shown about operation | movement of the analysis system which concerns on 2nd Embodiment.

(A) First Embodiment Hereinafter, a first embodiment of an energy utilization state analysis system and program according to the present invention will be described in detail with reference to the drawings. Note that the energy usage situation analysis system of the first embodiment is an analysis system.

(A-1) Configuration of the First Embodiment FIG. 1 is a block diagram showing connection relationships of various devices related to the first embodiment.

  In FIG. 1, an analysis system 10 and a plurality of monitoring target devices 40-1, 40-2, 40-3,... To be monitored in the analysis system 10 are connected to a network N. It is assumed that each monitoring target device 40 can communicate with each other. In the first embodiment, the number of monitoring target devices 40 to be arranged is not limited. Further, although the communication system of the network N is not limited, the network N will be described here as an IP network.

  In FIG. 1, monitoring areas Z1, Z2, Z3,... Represent areas divided by the monitoring target device 40. Note that the number of monitoring areas set in the first embodiment is not limited.

  In FIG. 1, the monitoring target device 40-1 is arranged in the monitoring area Z1. In addition, two monitoring target devices 40-2 are arranged in the monitoring area Z2. Furthermore, the monitoring target device 40-3 is arranged in the monitoring area Z3.

  The distribution board 105 is a device that distributes energy (electric power) from a power source (not shown) to the distribution boards 30-1, 30-2, 30-3,.

  The distribution boards 30-1, 30-2, 30-3,... Are apparatuses that distribute the energy (electric power) distributed from the distribution board 105 to the monitoring target devices 40 in the monitoring area. In addition, as the switchboard 20 and the distribution board 30, the existing thing is applicable.

  In FIG. 1, the electrical boards 30-1 to 30-3 are arranged respectively.

  In the following, the state of energy use (power use) for each distribution board 30 arranged in each monitoring area is also referred to as “system-specific energy use state”.

  Next, details of the analysis system 10 will be described.

  The analysis system 10 grasps the energy usage status for each system and the operating status of each monitoring target device 40, classifies the individual monitoring target devices 40 for each system, and makes it possible to grasp the details of the energy usage status. That is, in the analysis system 10, the monitoring target devices 40 existing on the network N can be directly grasped by communicating with each monitoring target device 40, but to which system (distribution panel 30) each monitoring target device 40 belongs. It is not possible to grasp directly. Therefore, the analysis system 10 performs a process of classifying which system each monitoring target device 40 belongs to by a configuration described later.

  The analysis system 10 includes an energy monitoring unit 101, an operation state monitoring unit 102, a storage unit 103, and an analysis unit 104.

  The analysis system 10 includes, for example, a program execution configuration such as a CPU, ROM, RAM, EEPROM, and hard disk, and an apparatus having an interface for connecting to a network (not limited to a single device, and a plurality of devices can be distributedly processed. In this case, it may be constructed by installing the energy utilization state analysis program of the embodiment, and even in that case, it is functionally shown as in FIG. Can do.

  The energy monitoring unit 101 monitors the energy usage status such as the amount of power used for each system. Here, it is assumed that the energy monitoring unit 101 generates time-series data that represents the power consumption (energy utilization status) of each system in time series. For example, the energy monitoring unit 101 may acquire data on the amount of power distributed to each of the distribution boards 30-1 to 30-3 from a measuring instrument or the like disposed on the distribution board 20.

  As a means for the energy monitoring unit 101 to grasp the energy usage status of each system, for example, in a system using an existing BEMS, a technique for grasping the energy usage status for each system may be applied. Specifically, the technology described in “JP-A-6-335060” (reference document 1) or “JP-A 2002-311069” (reference document 2) is applied to the energy monitoring unit 101. Also good.

  The operating status monitoring unit 102 monitors the operating status (for example, ON / OFF state) of each monitoring target device 40, and generates time series data representing the operating status in time series. The operating state monitoring unit 102 communicates with each monitoring target device 40 and grasps the operating status of each monitoring target device 40.

  For example, the operation status monitoring unit 102 confirms whether or not each monitoring target device 40 has a response to the PING command (ICMP ECHO) at regular or irregular intervals, and based on the confirmation result, You may make it grasp | ascertain an operating condition. For example, the operation status monitoring unit 102 determines that the monitoring target device 40 that has made a PING response is in an ON state and the monitoring target device 40 that has no PING response (for example, no response within a predetermined time) is in an OFF state. Also good.

  In addition, the operation status monitoring unit 102 uses the SNMP to monitor the device to be monitored 40 that supports SNMP (Simple Network Management Protocol) at regular or irregular intervals. MIB (Management Information Base) information acquisition may be acquired, and the operation status may be grasped based on whether or not MIB information can be acquired and the content of the acquired MIB information. The monitoring target device 40 may grasp the monitoring target device 40 in which the MIB information can be acquired as the ON state, and may grasp the monitoring target device 40 in which the MIB information cannot be acquired as the OFF state. Further, the monitoring target device 40 may acquire predetermined MIB information indicating the operating state of each monitoring target device 40, and may grasp the operating state of each monitoring target device 40 according to the content.

  The means for the operation status monitoring unit 102 to grasp the monitoring target device 40 existing on the network N is not limited. For example, a list of identification information such as the IP address of the monitoring target device 40 in advance is provided. May be registered, or may be grasped by inquiring of a network device (router, switch, etc.) arranged on the network N.

  The operation status monitoring unit 102 acquires, for example, information of an ARP (Address Resolution Protocol) table (ARP cache) of a network device arranged on the network N by using SNMP or the like, similar to an existing network management device (NMS). The node (monitoring target device 40) existing on the network N may be grasped from the IP address or MAC address of the acquired ARP table information. The operating status monitoring unit 102 may grasp the newly added monitoring target device 40 by periodically referring to the ARP table of each network device on the network N, for example.

  Note that in a normal office LAN or the like, a dynamic address may be assigned to a terminal such as a personal computer by a DHCP server (Dynamic Host Configuration Protocol) or the like in order to effectively use an address space. Therefore, even when the monitoring target device 40 does not move in particular, the IP address may be changed. Therefore, the operating status monitoring unit 102 grasps (for example, obtains using SNMP) not only the IP address but also the host name and MAC address for the monitoring target device 40 to which the dynamic IP address is assigned. It is desirable to keep it.

  The storage unit 103 has a function of storing processing results of the energy monitoring unit 101, the operating state monitoring unit 102, and the analysis unit 104.

  The storage unit 103 stores information (IP address or the like) related to the monitoring target device 40 on the network N, as well as time series data of the operating status of the monitoring target device 40, which is grasped by the operation state monitoring unit 102. In addition, the storage unit 103 stores time series data and the like of the energy usage status of each system, which is grasped by the energy monitoring unit 101.

  The analysis unit 104 uses the data stored in the storage unit 103 (data grasped by the energy monitoring unit 101 and the operating state monitoring unit 102) to determine which system (distribution panel 30) each monitoring target device 40 has. And each monitoring target device 40 is classified by system using the analysis result. Specific analysis processing contents performed by the analysis unit 104 will be described in the operation description to be described later.

(A-2) Operation of the First Embodiment Next, the operation of the analysis system 10 of the first embodiment having the above configuration will be described.

  First, after describing the overall operation of the analysis system 10, details of the analysis processing performed by the analysis unit 104 will be described.

(A-2-1) Outline of Operation of Analysis System FIG. 2 is a flowchart showing an outline of operation of the analysis system 10.

  FIG. 3 is a graph showing time series data accumulated in the analysis system 10 and intermediate data when analyzing the time series data.

  Here, it is assumed that the operation state monitoring unit 102 of the analysis system 10 already knows the existence of the monitoring target devices 40-1 to 40-3 connected to the network N.

  First, the energy monitoring unit 101 acquires the amount of energy (power amount) distributed from the distribution panel 105 to the distribution panel 106 as time-series energy usage status for each system, and stores it in the storage unit 103 as time-series data. (S101).

  The upper graph in FIG. 3A is a graph showing time-series data of the energy usage status of the system in the monitoring area Z1 (the power usage status of the system of the distribution board 30-1). In FIG. 3A, in the graph showing the energy utilization status of the system in the monitoring area Z1, the horizontal axis represents time. 3A, in the graph showing the energy usage status of the system in the monitoring area Z1, the vertical axis indicates the energy usage status of the system in the monitoring area Z1 with the maximum value (for example, the distribution board 30-1). Normalized by dividing by a predetermined value such as the maximum value of the amount of power that can be distributed, etc., and expressed by a value between 0 and 1. In the following, it is assumed that the time series data of the energy usage status in the monitoring area Z1 is represented as “f (t)”. Note that the lower graph in FIG. 3A is a graph used by the analysis processing of the analysis unit 104 described later.

  In FIG. 3, for the sake of simplicity, only the energy usage state related to the system of the monitoring area Z1 is illustrated, but the energy monitoring unit 101 also provides similar data for the systems of other monitoring areas. It shall be held.

  Then, the operation state monitoring unit 102 grasps the operation state such as the ON / OFF state and the power saving state of the monitoring target device 40 and stores it in the storage unit 103 as time series data (S102).

  The upper graph in FIG. 3B indicates time-series data of the operating status of the monitoring target device 40-1. Moreover, the upper graph of FIG.3 (c) shall show the time series data of the operation condition of the monitoring object apparatus 40-2. In FIG. 3B and FIG. 3C, the ON state is represented as “1” and the OFF state is represented as “0” for the time series data of the operation status of the monitoring target devices 40-1 and 40-2. Note that the lower graphs in FIGS. 3B and 3C are graphs used in the analysis processing of the analysis unit 104 described later.

  Hereinafter, the time series data of the operation status of the monitoring target device 40-1 shown in FIG. 3B is represented as “g (t)”. In addition, the time series data of the operation status of the monitoring target device 40-2 illustrated in FIG. 3C described above is represented as “h (t)”.

  In FIG. 3, only the operating status of the monitoring target devices 40-1 and 40-2 is illustrated for simplicity of explanation, but the operating status monitoring unit 102 also applies to other monitoring target devices 40. It is assumed that similar data is held.

  Then, the analysis unit 104 acquires time-series data regarding the energy usage status for each system (by monitoring area) and time-series data regarding the operating status of each monitoring target device 40 from the storage unit 103 (S103, S104). ).

  Next, the analysis unit 104 analyzes the presence / absence of correlation between the energy usage status of each system and the operating status of each monitoring target device 40 (S105). The details of the analysis processing by the analysis unit 104 will be described in detail in the operation description to be described later.

  Next, the analysis unit 104 classifies each monitoring target device 40 by system using the analysis result of step S105 described above. The classified results are stored in the storage unit 103 (S106). The details of the classification processing by the analysis unit 104 will be described in detail in the operation description to be described later.

(A-2-2) Processing Contents of Analysis Unit Next, details of the processing of the analysis unit 104 in the above-described steps S105 and S106 will be described.

  Here, as an example to be described first, description will be made using time series data of f (t), g (t), and h (t) shown in FIG.

  Here, the analysis unit 104 determines whether or not there is a correlation between the energy usage status f (t) of the system in the monitoring area Z1 and the operating statuses g (t) and h (t) of the monitoring target devices 40-1 and 40-2. In order to clarify the determination, the rate of change (differential value) of each of f (t), g (t), and h (t) is expressed as derivatives f ′ (t), g ′ (t), h ′ ( t). Further, the analysis unit 104 performs threshold processing on the derivative f ′ (t) related to the energy utilization status of the system in the monitoring area Z1, based on the value of the original time series data f (t), and varies by time zone. However, the time series data of the derivative f ′ (t) is obtained by setting 1 when the value is 0.5 or more and 0 when the value is less than 0.5. Note that the threshold value used in the above threshold processing is not limited.

  The time series data of the derivatives f ′ (t), g ′ (t), and h ′ (t) are shown in the lower graphs of FIG. 3 (a), FIG. 3 (b), and FIG. 3 (c), respectively. Can be shown as:

  The analysis unit 104 performs a process using the derivative as described above, so that the time at which the rate of change in the energy usage status becomes clear, such as the power ON / OFF time of each monitoring target device 40, is mainly set. As a comparison target, the presence / absence of correlation is determined. For example, the analysis unit 104 derives the derivative f ′ related to the energy utilization status of the system in the monitoring area Z1 with respect to the time series data of the derivatives g ′ (t) and h ′ (t) related to the operating status of each monitoring target device 40. The correlation with the time series data of (t) is obtained. And the analysis part 104 calculates | requires the combination with higher correlation, or the combination with a correlation more than predetermined as a combination with a correlation, The monitoring object apparatus which belongs to the subordinate of the system | strain of the monitoring area Z1 using the calculated | required combination. Analyze 40. For example, the analysis unit 104 determines that the correlation of the combination of g ′ (t) and f ′ (t) is higher than a predetermined value or higher than the correlation between h ′ (t) and f ′ (t). , G ′ (t) and f ′ (t) are determined to have a correlation. Then, the analysis unit 104 analyzes that the monitoring target device 40-1 belongs to the subordinate system of the monitoring area Z1 based on the combination determined to have a correlation.

  In the following, the time series data (time series data indicating the fluctuation state) of the derivatives f ′ (t), g ′ (t), and h ′ (t) are expressed as A, B, and C, respectively. And

  FIG. 4 is a graph extracted from FIG. 3 only for A = f ′ (t), B = g ′ (t), and C = h ′ (t).

  About the fluctuation | variation state of arbitrary measurement unit area of A, B, and C, it can represent with the matrix of the following (1) Formula, for example. Here, it is assumed that one unit of the above-described measurement unit section is 30 minutes, and the following equation (1) uses 30 minutes of data from 9:00:00 to 9:30 in FIG. 4 as a sample. .

  And the correlation (correlation of B and C with respect to A) of each change situation in the above-mentioned measurement unit section can be obtained by the following formula (2).

And the result of calculating the following formula (2) is obtained as the following formula (3).

In the analysis unit 104, for example, in the calculation result of the above formula (3), the case of 1 is correlated and the case of 0 is uncorrelated, whereby the presence or absence of correlation of each combination can be determined. For example, when the correlation between A and B is expressed as Y _AB , Y _AB = 1 from the calculation result of the above formula (3), so that the fluctuation status A related to the system of the monitoring area Z1 and the monitoring target device 40- For the fluctuation state B related to 1, a determination result that there is a correlation is obtained.

  Further, as shown in FIG. 4, by obtaining the average of the correlation for each calculation unit section, a combination having a high correlation can be obtained for the energy usage status of the monitoring area Z1 and the operating status of the monitoring target device 40.

About the average for every measurement unit area of the correlation of A, B, and C, it can obtain | require by the following (4) Formula. However, in the equation (4), A = f ′ (t), B = g ′ (t), C = h ′ (t), A (k) = f ′ (k), B (k) = g It is assumed that '(k), C (k) = h' (k). Further, in the equation (4), X (k) is represented by the following (5), and X (k) ⁺ is represented by the following (6) equation.

When the calculation of the above expression (4) is performed under the above-described conditions, the result becomes the following expression (7). The calculation result of the following equation (7) is expressed by a correlation coefficient Y, and indicates the correlation of each combination. For example, in the following equation (7), Y _AB represents the correlation between A relating to the system of the monitoring area Z1 and B relating to the monitoring target device 40-1.

Here, it is assumed that a calculation result such as the following expression (8) is obtained as a calculation result of the following expression (7). Here, as an example, the threshold value for determining whether or not there is a correlation in the analysis unit 104 is 0.3. That is, the analysis unit 104 determines that a combination having a correlation coefficient Y of 0.3 or more has a correlation and a combination having a correlation coefficient Y of less than 0.3 is determined to have no correlation. Then, Y _AB representing the correlation between A relating to the system of the monitoring area Z1 and B relating to the monitoring target device 40-1 is equal to or more than _0.3 when Y _AB = 0.3, and thus there is a correlation.

Next, the time series data of the derivatives related to the systems of the monitoring areas Z1, Z2 are represented as A1, A2, and the derivatives of the operating status of the monitoring target devices 40-1, 40-2, 40-3,. A case where the time series data is expressed as M1, M2, M3,... Will be described. In this case, when the correlation for each combination of the time-series data related to each monitoring area and the time-series data related to each monitored device 40 is calculated using the above equation (4), the calculation result is (9 )

  FIG. 5 is an explanatory diagram showing the contents of each component of the matrix of the above equation (9).

As shown in FIG. 5, in the above equation (9), Y _AnAm (where n and m are either 1 or 2) indicates the correlation between the time series data related to the system of the monitoring area. You may exclude from the analysis object regarding a correlation. For example, Y _A1A2 represents the correlation between the time series data A1 related to the monitoring area Z1 and the time series data A2 related to the monitoring area Z2.

In addition, as shown in FIG. 5, in the above equation (9), Y _MnMm (n and m are integers of 1 or more) indicates the correlation between the time series data related to the monitoring target device 40. You may exclude from the analysis object regarding correlation. For example, Y _M1M2 represents the correlation between the time series data M1 related to the monitoring target device 40-1 and the time series data M2 related to the monitoring target device 40-2.

For Y _AnMm and Y _MmAn (n is 1 or 2; m is an integer of 1 or more), the same combination of correlations is obtained and the same result is obtained. For the correlation of the time series data related to the monitoring target device 40, it is only necessary to obtain the correlation of any one of the combinations. For example, since Y _A1M1 and Y _M1A1 have the same result, it is not necessary to obtain both in order to calculate the presence or absence of correlation, and only Y _A1M1 may be obtained.

Therefore, the correlation between A1, A2 relating to the system of the monitoring areas Z1, Z2 and M1, M2, M3,... Relating to the monitoring target devices 40-1, 40-2, 40-3,. It can be obtained from the matrix shown in the equation.

Next, the time series data of the derivatives related to the monitoring areas Z1, Z2, Z3,... Are A1, A2, A3,... And the derivatives related to the monitoring target devices 40-1, 40-2, 40-3,. The correlation of each combination when the time series data of M1, M2, M3,. In this case, the correlation of each combination can be obtained from the matrix of the following equation (11) when calculated in a format excluding the excess correlation coefficient as in the above equation (10).

  From the calculation result of the above equation (11), for each column of M1, M2, M3,... Related to each monitored device 40, the correlation with the rows of A1, A2, A3,. And each monitoring object apparatus 40 can be classified into the system of the monitoring area with the highest correlation.

For example, here, it is assumed that the above equation (11) is calculated and a calculation result such as the following equation (12) is obtained.

  FIG. 6 is an explanatory diagram showing processing for analyzing correlated combinations using the matrix of the above equation (12).

  As shown in FIG. 6, in the above equation (12), for example, 0.29 is the highest correlation value in the column of M3 (monitoring target device 40-3). In this case, the analysis unit 104 determines that the monitoring target device 40-3 is in the system of the monitoring area Z4. That is, in the analysis unit 104, the monitoring target device 40-3 is classified into the system of the monitoring area Z4.

  Similarly, in the analysis unit 104, A3 (monitoring area Z3) in the column of M1 (monitoring target device 40-1), A2 (monitoring area Z2), and M4 (monitoring target) in the column of M2 (monitoring target device 40-2). The apparatus 40-4) can be classified into A5 (monitoring area Z5), and the M5 (monitoring target apparatus 40-5) can be classified into A1 (monitoring area Z1).

  Note that the analysis unit 104 cannot classify the monitoring target device 40 in a column related to the monitoring target device 40 when there is no correlation coefficient equal to or greater than a predetermined threshold (classify into any monitoring area system). May be determined).

  Then, the analysis unit 104 registers the classified contents in the storage unit 103. The storage format of the classified contents in the storage unit 103 is not limited. For example, each monitoring target device 40 is stored as table information indicating which monitoring area system is classified. Also good. Further, if there is a table in which the address information and the like of each monitoring target device 40 is registered together in the storage unit 103, a field indicating the system of the monitoring area to which the table belongs may be provided and stored.

(A-3) Effects of First Embodiment According to the first embodiment, the following effects can be achieved.

  In the first embodiment, a combination (correlated combination) in which the tendency of the energy usage situation for each system and the operation status of each monitoring target device 40 match is detected, and individual monitoring is performed based on the detected combination. The target device 40 can be classified by system.

  Thereby, for example, it is not necessary for the system administrator to perform an operation of classifying the individual monitoring target devices 40 by system. Furthermore, the system administrator can grasp the detailed energy usage status of each system, such as which system energy is used by each monitored device 40 in which time zone, and control from the energy usage status for each system. More detailed energy management can be performed.

(B) Second Embodiment Hereinafter, a second embodiment of the energy utilization status analysis system and program according to the present invention will be described in detail with reference to the drawings. Note that the energy usage situation analysis system of the second embodiment is an analysis system.

(B-1) Configuration of Second Embodiment FIG. 7 is a block diagram showing connection relationships of various devices related to the second embodiment.

  Hereinafter, the difference between the second embodiment and the first embodiment will be described.

  The second embodiment is different from the first embodiment in that the analysis system 10 is replaced with an analysis system 10A.

  The analysis system 10A is different from the first embodiment in that the operation state monitoring unit 102 and the analysis unit 104 are replaced with the operation state monitoring unit 102A and the analysis unit 104A.

  As shown in FIG. 7, the monitoring target device 40-1 is arranged in the system of the monitoring area Z1. In addition, monitoring target devices 40-2 and 40-3 are arranged in the system of the monitoring area Z2. Further, the monitoring target device 40-4 is arranged in the system of the monitoring area Z3.

  Here, segments divided on the IP network N (for example, networks partitioned by L3 switches and routers) are formed in the systems of the monitoring areas Z1, Z2, and Z3, respectively. Assume that different network addresses (address spaces) are assigned. That is, the IP address of the network address of the system is assigned to the monitoring target device 40 belonging to the system of each monitoring area.

  For example, a segment related to the monitoring area Z1 is assigned 192.168.1.0 as a network address, and if the subnet mask length is 24 bits, the address space of this segment is 192.168.1.0. To 192.168.1.255. Then, it is assumed that 192.168.1.12 is assigned to the monitoring target device 40-1 of the system in the monitoring area Z1 from this address space.

  In addition, 192.168.2.0 and 192.168.3.0 (the subnet mask length is assumed to be 24 bits for both) are assigned to the segments related to the monitoring areas Z2 and Z3, respectively. It shall be. That is, the address space of the segment related to the monitoring area Z2 is 192.168.2.0 to 192.168.2.255, and the address space of the segment related to the monitoring area Z3 is 192.168.3.0 to 192.168.3.255. And it is assumed that 192.168.3.37 and 192.168.3.38 are assigned to the monitoring target devices 40-2 and 40-3 of the system in the monitoring area Z2, respectively. Furthermore, it is assumed that 192.168.3.82 is assigned to the monitoring target device 40-4 of the system in the monitoring area Z3.

  Hereinafter, the monitoring target device 40 that is not yet classified into any system is referred to as an “unclassified monitoring target device”. Further, the monitoring target device 40 that has already been classified into one of the monitoring area systems is referred to as an “already classified monitoring target device”. As an uncategorized monitoring target device, for example, a new one is added after the processing shown in the flowchart of FIG. 2 described above (processing for classifying each monitoring target device 40 by system) (connected to a segment in any monitoring area). 2 and the monitoring target device 40 that could not be classified by the process shown in the flowchart of FIG. 2 (for example, the energy utilization status of any system is not correlated with a predetermined threshold or more and is classified. Applicable if not possible).

  The operating state monitoring unit 102A grasps the network address related to each monitoring target device 40 based on the IP address of each monitoring target device 40 in addition to monitoring the operating status of each monitoring target device 40. Further, the operating state monitoring unit 102A detects each newly added monitoring target device 40 after performing the processing shown in FIG. 2 as an unclassified monitoring target device. Then, the operating state monitoring unit 102A stores the detected content in the storage unit 103.

  The analysis unit 104A detects the monitoring target device 40 that could not be classified by the process shown in the flowchart of FIG. 2 as an unclassified monitoring target device.

  And about the uncategorized monitoring object apparatus detected by the analysis part 104A and the operation state monitoring part 102A, the analysis part 104A classify | categorizes as belonging to the system | strain of the same monitoring area as the already classified monitoring object apparatus in the same segment.

(B-2) Operation of the Second Embodiment Next, the operation of the analysis system 10A of the second embodiment having the above configuration will be described.

  FIG. 8 is a flowchart showing an outline of the operation of the analysis system 10A.

  In FIG. 8, steps S201 to S206 are the same as steps S101 to S106 in the first embodiment (the above-described FIG. 2), and thus description thereof is omitted.

  Then, the analysis unit 104A detects, as the unclassified monitoring target device, the monitoring target device 40 that could not be classified in the processing of steps S201 to S206 described above (S207). Note that the analysis unit 104A also grasps the uncategorized monitoring target device (the monitoring target device 40 added to one of the monitoring areas after the processing of the above-described steps S201 to 206) detected by the operating state monitoring unit 102A. In addition, it may be added to the target of processing to be described later.

  Here, as an example, it is assumed that the monitoring target devices 40-1, 40-2, and 40-4 are already classified monitoring target devices and only the monitoring target device 40-3 is detected as an unclassified monitoring target device.

  Next, the analysis unit 104A detects an already-classified monitoring target device (an already-classified monitoring target device having a common network address) in the same segment as the unclassified monitoring target device based on the content stored in the storage unit 103. (S208).

  Here, as the unclassified monitoring target device 40-3 (IP address: 192.168.2.38) as the unclassified monitoring target device, the network address is the same as “192.168.8.2. "0" is detected as the monitoring target device 40-2 (IP address: 192.168.2.37).

  Next, the analysis unit 104A classifies the unclassified monitoring target device into the same monitoring area system as the already classified monitoring target device in the same segment detected in step S208, and stores the classified result in the storage unit 103. (S209).

(B-3) Effects of Second Embodiment According to the second embodiment, the following effects can be obtained in addition to the effects of the first embodiment.

  In the analysis system 10A, when an unclassified monitoring target device is detected, it is classified into the same system as the already classified monitoring target devices in the same segment on the network N. Thereby, in the analysis system 10A, since it is not necessary to perform the calculation processing of the presence / absence of correlation (the processing of steps S201 to S206) every time a new monitoring target device 40 is added on the network N, the processing amount is reduced. Or the energy usage status can be easily grasped.

(C) Other Embodiments The present invention is not limited to the above-described embodiments, and may include modified embodiments as exemplified below.

(C-1) In the second embodiment, a process of classifying each monitoring target device for each system using the correlation between the energy usage status of each system and the operating status of each monitoring target device (step S201 described above) To S206), the network address is used to classify the unclassified monitoring target devices by system. However, the network address may be used for classification first.

  For example, first, each monitoring target device is classified by network address, and each monitoring target device is picked up for each network address, and only the picked-up monitoring target device has a correlation with the energy utilization status of each system. It is also possible to classify by performing the process of obtaining the above (the process of steps S201 to S206 described above). In this case, each monitoring target device that has not been picked up can be classified into the same system as the already-classified monitoring target devices in the same segment. As a result, even when there is a system in which a plurality of monitoring target devices are installed, it is not necessary to obtain the correlation with the energy usage status of each system for all the monitoring target devices, so that the processing amount can be reduced. .

(C-2) In each of the above-described embodiments, for the sake of simplicity, it has been described that only one distribution board is arranged in each monitoring area, but a plurality of distribution boards may be arranged. In that case, it is desirable for the analysis unit to register and manage in advance which monitoring area each system belongs to.

  DESCRIPTION OF SYMBOLS 10 ... Analysis system, 101 ... Energy monitoring part, 102 ... Operating condition monitoring part, 103 ... Memory | storage part, 104 ... Analysis part, 20 ... Distribution board, 30, 30-1-30-30 ... Distribution board, 40-1 40-3, 40 ... monitoring target devices, Z1 to Z3 ... monitoring areas.

Claims (7)

Distribution status data holding means for holding distribution status data for each distribution device that distributes energy to one or a plurality of monitoring target devices, in which time-series values relating to the amount of energy distributed by the distribution device are stored;
Operating status data holding means for holding operating status data representing the operating status of each of the monitoring target devices in time series;
Analyzing the relationship between the distribution status data for each distribution device held by the distribution status data holding means and the operation status data for each monitored device held by the operation status data holding means, and using the analysis result And a classifying means for classifying which of the distribution devices each of the monitoring target devices belongs to.   The classification means calculates the correlation for each combination of the operation status data of each of the monitoring target devices and the distribution status data of each of the distribution devices, and uses the calculation result to determine whether each of the monitoring target devices 2. The energy utilization status analysis system according to claim 1, wherein the system belongs to which distribution device belongs.   The energy according to claim 1 or 2, wherein the distribution status data is expressed by a value obtained by dividing a value of an energy amount distributed by the distribution device for each time series by a predetermined value and normalizing the energy amount. Usage analysis system.   The operating status data is a first value indicating that the monitored device is in an operating state for the operating state of the monitored device for each time series, or a first value indicating that the monitored device is in a stopped state. The energy utilization state analysis system according to any one of claims 1 to 3, wherein the energy utilization state analysis system is represented by any one of the two values.   The classification means obtains a derivative of each time series data, obtains the presence / absence of correlation for each combination of each derivative and each distribution status data, and based on the combination having the correlation, each of the monitoring target devices The energy utilization situation analysis system according to claim 4, wherein the distribution apparatus belongs to which of the distribution devices. A segment recognizing unit for recognizing a segment on the network to which each of the monitoring target devices is connected;
The classification means is the same among the monitoring target devices that have already been classified as to which distribution device the management target device that has not been classified yet belongs to which distribution device. It classifies | categorizes as belonging to the same said distribution apparatus as what is connected to the segment. The energy usage condition analysis system in any one of Claims 1-5 characterized by the above-mentioned. The computers that make up the energy usage analysis system
Distribution status data holding means for holding distribution status data for each distribution device that distributes energy to one or a plurality of monitoring target devices, in which time-series values relating to the amount of energy distributed by the distribution device are stored;
Operating status data holding means for holding operating status data representing the operating status of each of the monitoring target devices in time series;
Analyzing the relationship between the distribution status data for each distribution device held by the distribution status data holding means and the operation status data for each monitored device held by the operation status data holding means, and using the analysis result An energy utilization status analysis program that causes each of the monitoring target devices to function as a classification unit that classifies which of the distribution devices belongs to.

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