JP5375139B2 - Data collection management device and missing data interpolation processing method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To improve precision of interpolation data by executing proper interpolation processing, according to deficiency pattern. <P>SOLUTION: This data collection and control device is provided with: an energy consumption quantity collection means for collecting the total energy consumption quantity data of each group which is obtained by dividing a plurality of pieces of equipment into one or more groups and the individual energy consumption quantity data belonging to each group in a prescribed cycle; a deficiency data interpolation means for, when data deficiency is detected in data collected by the energy consumption quantity collection means, calculating interpolation data by interpolation processing predetermined for each deficiency pattern; and a display means for discriminating and displaying the data normally collected by the energy consumption quantity collection means and interpolation data calculated by the deficiency data interpolation means. <P>COPYRIGHT: (C)2010,JPO&amp;INPIT

Description

  The present invention relates to a technique for measuring, recording and displaying energy consumption in a backyard such as a supermarket, and in particular, data that can be collected by interpolating with high accuracy even when a deficiency occurs in the process of collecting measured values. The present invention relates to a collection management device and a missing data interpolation processing method.

  When measuring the energy consumption of a device in a backyard such as a supermarket, data on the energy consumption of each device and the energy consumption of the entire device collected from the main are collected. At this time, when a defect occurs in the measurement data, it is common to interpolate using the previous value.

  Regarding conventional energy consumption measurement technology, for example, in Patent Document 1, even if the layout conditions of an area where a plurality of departments are located fluctuate, energy use facilities provided for the area In order to calculate the individual energy consumption for each department out of the total energy consumption, store the usage ratio of the individual energy consumption of each department to the total energy consumption, and use this ratio for the total energy consumption. There has been proposed a method and apparatus for calculating the individual energy consumption of each department whose layout has been changed by multiplying.

  Further, in Patent Document 2, in order to interpolate the missing energy consumption amount, for example, based on attributes such as the number of owned kitchen appliances, the old attribute consumption energy amount and the new attribute consumption energy amount are collected. , A method of calculating the consumed energy amount of the new attribute corresponding to the consumed energy amount of the old attribute based on the standard value of the new attribute, the consumed energy amount of the old attribute and the standard value by the CPU, and interpolating the missing part, In addition, a method is disclosed in which the old attribute standard value, the new attribute energy consumption amount and the standard value are calculated based on the old attribute energy consumption amount corresponding to the new attribute energy amount and the missing portion is interpolated. ing.

JP 2004-340595 A JP 2003-269991 A

  The above-mentioned conventional technique calculates other data using a certain data. However, when collecting and managing backyard data, there are various ways of data loss. It is necessary to perform highly accurate data interpolation according to (missing pattern).

  In addition, since it is necessary to monitor measurement data in real time, it is also necessary to continue the monitoring process by interpolating the data immediately when a defect occurs.

  The present invention has been made in view of the above-described circumstances, and improves the accuracy of interpolation data by executing an appropriate interpolation process according to a missing pattern, thereby enabling more accurate monitoring work. It is an object of the present invention to provide a management device and a missing data interpolation processing method.

  In order to achieve the above object, the data collection and management apparatus according to the present invention includes a plurality of devices divided into one or more groups, and the total energy consumption data of each group and the individual energy consumption of devices belonging to each group. Energy consumption collecting means for collecting data at a predetermined cycle; and when data loss is detected in the data collected by the energy consumption collecting means, interpolation data is obtained by interpolation processing predetermined for each missing pattern. It is characterized by comprising a missing data interpolation means for calculating, a display means for identifying and displaying the data normally collected by the consumed energy amount collecting means and the interpolation data calculated by the missing data interpolation means.

  In the present invention, when data loss is detected, interpolation processing is performed according to a predetermined procedure for each missing pattern, and the interpolation data and normally received data are identified and displayed. Processing is possible, and the person in charge of management can know whether the data is normally received or calculated by interpolation processing, so that a correct determination can be made.

  The data collection and management apparatus according to the present invention further includes means for storing history information of energy consumption for each individual device, and apportionment of the individual energy consumption of each device relative to the total energy consumption based on the history information. A distribution coefficient calculation unit for calculating a coefficient, and the missing data interpolation unit performs an interpolation process using the distribution coefficient.

  In the present invention, an apportioning coefficient is calculated from the history information of the consumed energy amount, and interpolation processing is executed. This enables interpolation processing even when data of a plurality of devices in the same group is lost simultaneously.

  Note that the apportionment coefficient may be a value set in advance by the person in charge of management, but preferably, the history of energy consumption assumes, for example, that the proportion of equipment used varies depending on the season, weekdays, holidays, etc. It is preferable to take the average value of the data of each period at every predetermined time. Thereby, highly accurate interpolation processing is possible. When the apportioning coefficient set by the manager is used as an initial value and the apportioning coefficient calculation means that is executed periodically finds an apportioning coefficient, this apportioning coefficient can be used as time passes. A highly accurate distribution coefficient can be applied.

  The data collection management device according to the present invention further includes a change rate calculation means for calculating a change rate of the previous value and the current value of the total energy consumption at each collection timing, and the missing data interpolation means includes a certain collection timing. When all data in the same group is detected in step 1, the latest change rate is calculated from the change rates calculated by the change rate calculation means, and the total energy consumption of the group is calculated. The individual energy consumption amount of each device is calculated by applying an apportioning coefficient of each device belonging to the group to the energy amount.

  In the present invention, the rate of change of the total energy consumption is recorded at any time, and by using this rate of change and the apportioning coefficient of each device, accurate data interpolation is possible even when all data is missing.

  Preferably, the missing data interpolation means adds reliability corresponding to the interpolation processing to the calculated interpolation data, and the display means adds the reliability added to the interpolation data when identifying and displaying the interpolation data. The data should be identified and displayed based on the above.

  Thereby, since the person in charge of management can know not only whether or not the data is interpolation data but also the reliability of the interpolation data, it can be used for monitoring work.

  When the reliability is equal to or less than a predetermined value, recalculation is performed using the data collected at the later collection timing, for example, using the average of the distribution coefficient of the subsequent data and the distribution coefficient during the interpolation process. It is preferable to correct the interpolation data by such a process. Thereby, the accuracy of the interpolation data can be improved.

  The missing data interpolation processing method according to the present invention is a missing data interpolation processing method that collects energy consumption data of a plurality of devices, detects missing data, and interpolates the missing data. Dividing the equipment into one or more groups, collecting the total energy consumption data of each group and the individual energy consumption data of the equipment belonging to each group in a predetermined cycle, and the energy consumption for each individual equipment Saving historical information, calculating the apportioning coefficient of the individual energy consumption of each device relative to the total energy consumption based on the history information, and the previous and current values of the total energy consumption at each collection timing The step of calculating the rate of change of the value, and when data loss is detected in the collected data, it is predetermined for each missing pattern When all data in the same group is detected at a certain collection timing while performing inter-process processing, the total energy consumption of the group is calculated using the latest change rate among the calculated change rates. After that, the step of executing the interpolation process for calculating the individual energy consumption amount of each device by applying the apportioning coefficient of each device belonging to the group to the total energy consumption amount, the normally collected data, and the interpolation processing And the step of identifying and displaying the data calculated by the above.

  According to the present invention, since the interpolation process corresponding to the pattern is executed according to the missing pattern, the accuracy of data interpolation can be improved.

  Even if all data is lost at a certain collection timing, the monitoring work is continued by interpolating the missing data by the apportioning coefficient of the most recent change rate of total energy amount and individual energy consumption of past devices. In addition, more accurate monitoring work can be performed by identifying and displaying the interpolated data.

  In addition, the data interpolation pattern is associated with the reliability, and the reliability less than a predetermined value can be corrected with other data collected thereafter, thereby enabling highly accurate data collection management.

It is a functional block diagram of the data collection management apparatus 1 by the 1st Embodiment of this invention. It is a data structural example of the setting table 19 of FIG. It is a data structural example of measurement energy DB21 of FIG. It is a flowchart which shows the process sequence by Example 1 of the missing data interpolation means 12 of FIG. It is a flowchart which shows the process sequence by Example 2 of the missing data interpolation means 12 of FIG. It is a flowchart which shows the process sequence by Example 3 of the missing data interpolation means 12 of FIG. It is a data structural example of energy consumption DB22 of FIG. It is an example of the screen displayed on the display screen 30 of FIG. It is a functional block diagram of the data collection management apparatus 1 by the 2nd Embodiment of this invention. 10 is a data configuration example of a distribution coefficient calculation setting table 23c stored in a distribution coefficient calculation DB 23 of FIG. FIG. 11 is a relationship diagram between the average consumption energy amount table 23a and the distribution coefficient calculation table 23b stored in the distribution coefficient calculation DB 23 of FIG. 9, and FIG. 11 (a) is a data configuration diagram of the average consumption energy amount table 23a. (B) is a data block diagram of the distribution coefficient calculation table 23b. It is the figure showing the process outline | summary of the proportionality coefficient calculation means 14 of FIG. It is a specific data example of the apparatus 1 (flyer) of the average consumption energy amount table 23a of Fig.11 (a). It is an example of the specific apportioning coefficient of the apparatus which belongs to the same group of the apportioning coefficient calculation table 23b of FIG.11 (b). It is a functional block diagram of the data collection management apparatus 1 by the 3rd Embodiment of this invention. It is a data example of the proportionality coefficient calculation table 23b by the 3rd Embodiment of this invention. It is a flowchart which shows the process sequence of the missing data interpolation means 12 of FIG. It is an example of data of energy consumption DB22 by the 3rd Embodiment of this invention. It is a data example of measurement energy DB21 by the 3rd Embodiment of this invention. It is an example of data of energy consumption DB22 after the correction process by the interpolation data correction means 16 of FIG.

  Embodiments of the present invention will be described below. FIG. 1 is a functional block diagram of a data collection management device 1 according to the first embodiment.

  In this figure, the data collection management device 1 includes a sensor 2 that detects energy consumption data for each predetermined group such as a department, using data on the energy consumption of the measurement target devices (the main trunk and devices in the figure) of the backyard 50. Input through. The data is group No. at the collection time. , And the component No. identifying the device in the group. The data value of the energy consumption amount detected by the sensor in association with is sent and sent. At this time, if a defect occurs in the collected data due to noise or a malfunction of the device, the missing data is interpolated by an interpolation process described later, and a series of data is stored and managed and displayed on the screen. The manager in charge monitors the operating state of the measurement target device and the presence / absence of an abnormality based on the energy consumption output on the display screen.

Next, the configuration of the data collection management device 1 will be described.
The data collection management device 1 receives energy consumption amount data sent from the measurement target device sensor 2 directly or via a transmission device (not shown), and saves it in the measurement energy DB 21. The occurrence of deficiency is detected, deficient data interpolation is performed based on the collected data, and the result and collected data are stored in the consumed energy amount DB 22. Display means 13 for outputting the quantity to the display screen 30 of the display device is provided. Each means 11-13 is a function realizable by a program.

  The data collection management device 1 also includes a setting table 19 that stores various parameters for grouping measurement target devices and collecting data.

  FIG. 2 is a data configuration example of the setting table 19. In addition to the name of each measurement point, the measurement point ID for management by a program or database, physical address information used for input processing or transmission, the group number of each measurement point. And the component No. that identifies the device in it. And proration coefficients are stored in association with each other. This apportioning coefficient indicates the proportion of energy consumption of each device and is set by the person in charge of management. The physical address information is an address determined by the specifications of the sensor used, such as a watt hour meter. It becomes a combination. Group No. in group configuration definition Is an identifier mainly indicating a department, and a component number. Is an identifier indicating a device in a department (group). In this figure, “0” indicates the entire department, and “1” and thereafter indicate each device.

Next, the operation of the data collection management apparatus 1 having the above configuration will be described.
(Energy consumption collection processing)
The consumed energy amount collecting means 11 collects the consumed energy amount at a predetermined time and stores the collected data in the measured value column of the corresponding group configuration definition in the measured energy DB 21 illustrated in FIG. . Further, information indicating whether or not the amount of energy consumption has been successfully collected is stored in association with each measured value column. In this figure, “0” is stored in the result column when the data is normally received at a predetermined collection timing, and “1” is stored as an abnormality otherwise.

(Missing data interpolation process)
Hereinafter, the processing procedure of the missing data interpolation unit 12 will be described for each embodiment.

First, the processing procedure of the missing data interpolation unit 12 according to the first embodiment will be described with reference to FIG.
When the missing data interpolation means 12 is activated after a lapse of a certain time from the collection timing, it accesses the measurement energy DB 21 and refers to the result column of each measurement value at the collection timing to determine whether the received data is normal or abnormal. judge. When all the result columns of the collection timing are “0”, that is, normal, it is determined that there is no deficiency (“No” in S101), and the energy consumption amount of each measurement target device is extracted from the measurement value, or predetermined such as scale conversion (S102) and stored in the corresponding column of the energy consumption DB 22 (S103).

On the other hand, in step S101, if the result column is “1”, that is, abnormal, it is determined that there is a defect (“Yes” in S101), and then it is determined whether the data is group-defined (S104). When the group is not defined, that is, when the data is collected alone (“No” in S104), the previous collection timing data (previous value) is calculated (S105) and stored in the energy consumption DB 22 ( S103).
If the group is defined in step S104, it is determined whether or not the number of abnormalities in the series of result columns of the collection timing is only one (S106). If there is only one, the following relational expression (1) is also obtained. One result is calculated (S107), and all measurement data of the collection timing including the calculation result is stored in the energy consumption DB 22 (S103).
Relational expression Total (main) energy consumption = Energy consumption of equipment in the Σ group
(1)

  FIG. 5 shows a processing procedure of the missing data interpolation unit 12 according to the second embodiment. In this figure, the processes of steps S201 to S205 correspond to the processes of steps S101 to S105 of FIG.

  If the group is defined in step S204 (“Yes” in S204), then there is a plurality of missing data at the same collection timing, and at least one of the series of measurement data has been measured. (S206), in the case of Yes, it calculates using the proportionality coefficient preserve | saved in the setting table 19 (S207), and saves a series of measurement data including the calculation result in energy consumption DB22. .

  In step S207, if the total energy consumption is measured as a method of calculation using the distribution coefficient, the individual energy consumption of each apparatus is calculated by multiplying this measured value by the distribution coefficient of each apparatus. . If only the energy consumption of a device is measured, multiply the measured value of that device by the ratio of the apportioning factor of that device to the apportioning factor of the other device to calculate the energy consumption of that other device. . Further, the total energy consumption can be calculated by dividing the measured value of the measured device by the apportioning coefficient of the device.

Next, as Example 3, an example in which Example 1 and Example 2 are combined is shown in FIG.
In FIG. 6, steps S301 to S307 are the same as steps S201 to S207 in FIG. Steps S308 and S309 are the same as steps S206 and S207 in FIG.

  That is, the missing data restoration accuracy can be improved by interpolating the missing data by switching between the processes A, B, and C described in FIG. In the example of FIG. 6, process A has the highest accuracy, followed by process B and process C in this order.

  The following describes how interpolation processing is performed for each missing case. In the following, the numbers of the calculation examples correspond to the calculation example numbers given in the right column of FIG. Further, for the interpolation processing, corresponding processing symbols (A to C) shown in FIG. 6 are given.

a. When there is no deficiency (calculation example 0)

  When there is no deficiency, the measured value is stored as it is in the energy consumption DB 22 and a flag “0” indicating no interpolation is stored.

b. When the defect is the main trunk only (Calculation Example 1) (Processing A)

  In Table 2, when only the entire (main trunk) cannot be collected, the energy consumption of the entire (main trunk) is obtained by summing up the energy consumption of the devices in the group from Equation (1).

c. When the defect is only device 2 (calculation example 2) (Processing A)

  In Table 3, if only device 2 could not be collected, subtract the total energy consumption of device 1 and device 3 from the total (main) energy consumption based on equation (1). The amount of energy consumed by the device 2 is obtained.

d. When only total energy consumption data can be collected (Calculation Example 3) (Processing B)

  When only the total energy consumption within the group can be collected, each missing individual energy consumption is calculated based on the total energy consumption by multiplying each individual energy consumption by a set proportionality factor.

e. When only one of a plurality of individual energy consumptions can be collected (Calculation Example 4) (Processing B)
When one of multiple individual energy consumptions can be collected, based on this collected individual energy consumption, the apportioning coefficient set for the individual energy consumption is divided to restore the total energy consumption Then, based on the restored total consumed energy amount, an individual energy consumption amount that could not be collected is calculated by multiplying a proportionality coefficient set for each individual consumed energy amount.

  In the example of Table 5, based on the calculation formula in Table 5, from the individual energy consumption “42” of the device 2 and the apportionment coefficient “30%”, the total energy consumption “140” of the whole (main) first. Ask for. The individual energy consumption amount is calculated by multiplying this by the apportioning coefficient of the device 1 or the device 3.

f. When all data is missing (Calculation Example 5) (Processing C)
When the energy consumption amounts of all the devices are lost, the respective acquired energy amounts are allocated to the lost energy consumptions and restored.

  Since all are missing, the current value = previous value, and the amount of energy consumed is calculated as “0” by this value−previous value.

(Other calculation examples)
Note that the above calculation method is an example, and the amount of energy consumption can be obtained using another calculation formula. For example, if even one of the individual energy consumptions shown in Table 7 below is measured, the total amount used may be obtained first, and then apportioned by the apportioning coefficient of each device. It is also possible to obtain a difference from the individual consumption of the device being measured, and obtain the individual energy consumption amount from the ratio of the apportioning coefficient of the defective device.

  Although it can be obtained from the ratio between the apportioning coefficient of the used amount of the device being measured and the apportioning factor of the missing device, in this embodiment, an apportioning factor preset by the manager is used. Therefore, the accuracy is lowered, which is not preferable.

Therefore, it is preferable to determine a highly accurate algorithm in advance according to the missing pattern and perform interpolation using this algorithm.

  As described above, the missing data interpolation unit 12 performs the interpolation process for each missing pattern, and stores the result and the measurement data in the energy consumption DB 22. In addition, a flag indicating interpolation data or measurement data is stored in association with each data.

FIG. 7 is a data example of the energy consumption DB 22 and is shown in correspondence with FIG. A series of data is stored in the energy consumption DB 22 by interpolating the missing data. The calculation example in the rightmost column of the table corresponds to FIG. 3 and the description of each calculation example described above.
In FIG. 7, the energy consumption DB 22 has data of a flag (1: interpolation, 0: no interpolation) indicating whether or not the date and time and the energy consumption per predetermined time are interpolated.

(Energy consumption display processing)
The display unit 13 displays the energy consumption amount of each measurement target device stored in the energy consumption amount DB 22 on the display screen 30.

  FIG. 8 is an example of a display screen displayed on the display device. Here, the missing data portion is identified and displayed as “●”, so that the person in charge of management can visually confirm the amount of energy consumption while looking at a table or a graph.

  As described above, according to the present embodiment, when data loss is detected, interpolation processing is performed according to a predetermined procedure for each loss pattern, and the interpolation data and normally received data are identified and displayed. Appropriate interpolation processing is possible depending on the pattern. In addition, since the person in charge of management can know whether the data is normally received or the data calculated by the interpolation process, a correct determination can be made.

Next, a second embodiment will be described.
FIG. 9 is a functional block diagram of a data collection management device according to the second embodiment. The main difference from FIG. 1 is that a proportional coefficient calculation that calculates the ratio of the energy consumption amount of each device to the total energy consumption from time to time and stores it in the proportional coefficient calculation DB 23 based on the collected energy consumption data. Means 14 is added. Since other configurations are the same as those in FIG. 1, the same elements are denoted by the same reference numerals and description thereof is omitted.

  In the present embodiment, a distribution coefficient calculation setting table 23c illustrated in FIG. 10 is provided in the distribution coefficient calculation DB 23 so that the distribution coefficient can be managed for each period. In FIG. 10, periods 1 to 4 specify whether the distribution coefficient calculated by the distribution coefficient calculation means 14 is common to the year or divided by period, and the start date and end date of each period are set. FIG. 10 shows an example of designation common to the years. For example, when dividing by period, period 1: 4/1 to 6/30, period 2: 7/1 to 9/30, period 3: 10/1 to 11 / 30, period 4: 12/1 ~ 3/31, etc., can be divided according to the season.

  Next, the configuration of the distribution coefficient calculation DB 23 will be described with reference to FIG. The distribution coefficient calculation DB 23 has an average energy consumption amount table 23a and a distribution coefficient calculation table 23b. Corresponding to the period of the apportioning coefficient calculation setting table 23c, the data is managed in each of the periods 1 to 4. FIG. 11 illustrates the period 1 as an example, but the same applies to other periods (periods 2 to 4).

  The average energy consumption table 23a has a predetermined cycle (for example, every hour) in a management unit in which the proportion of the energy consumption may change depending on the operation state of the device, such as weekdays and holidays, for each device. Data on average energy consumption can be stored.

  On the other hand, the apportioning coefficient calculation table 23b can store the ratio of the average energy consumption amount for each predetermined period of the devices in the same group for each management unit based on the data of the average energy consumption amount table 23a. ing.

Next, in the data collection management device 1 having the above-described configuration, the processing procedure of the distribution coefficient calculation unit 14 will be described.
FIG. 12 is a diagram showing an outline of the processing of the apportioning coefficient calculating means 14. In this figure, when the apportioning coefficient calculating means 14 is activated after a predetermined time from the predetermined collection timing, the average energy consumption calculating process (S401) described below with reference to the setting table 19 and the apportioning detection calculating DB 23 is performed. Then, a distribution coefficient calculation process (S402) is executed.

(Average energy consumption calculation process)
The average energy consumption calculation process is performed on data that has been normally collected at all measurement points of group-defined constituent elements among the data stored in the measurement energy DB 21 at each collection timing. If the measurement data of 9:00 o'clock on October 15, 2008 for equipment 1 (flyer), equipment 2 (multi-purpose oven), and equipment 3 (tableware washing machine) of Fig. 2 is successfully collected, When the period is determined with reference to the proportional coefficient calculation setting table 23c in FIG. Also, since October 15, 2008 is a Wednesday, it will be a weekday. Recalculate the average value from the current average energy consumption amount of each of the devices 1, 2, and 3 of the period 1 of the average energy consumption amount table 23a of the apportioning coefficient calculation DB 23 and the current day, and the current energy consumption amount, This is reflected in the table 23a. For example, if the current average energy consumption at period 1 and device 1 at 9 o'clock on weekdays is 79 and the data for 5 days is 85 this time, recalculating the average value gives (79 x 5 + 85) / 6 = 80.

  FIG. 13 is a specific data example of the device 1 (flyer) in the average energy consumption table 23a.

(Proportional coefficient calculation process)
The apportioning coefficient calculation process is activated upon completion of the average energy consumption calculation process. The distribution coefficient is recalculated from the average energy consumption of each device in a predetermined management unit (for example, period 1, weekday) in the average consumption energy amount table 23a of the distribution coefficient calculation DB 23, and is reflected in the distribution coefficient calculation table 23b. The apportioning coefficient preset in the setting table 19 may be used as the initial value of the table 23b.

  For example, the average values for period 1 and weekdays at 9:00 are equipment 1 (flyer) = 80 [kWh], equipment 2 (multipurpose oven) = 103 [kWh], equipment 3 (dishwasher) = 7 [kWh] When the distribution coefficient is recalculated, the device 1 (flyer) = 42%, the device 2 (multipurpose oven) = 54%, and the device 3 (dishwasher) = 4%.

  FIG. 14 is an example of specific apportioning coefficients of devices belonging to a predetermined group in a predetermined management unit (in this example, period 1, weekday).

(Missing data interpolation process)
In the second and third examples of the first embodiment, the missing data interpolation unit 12 executes the interpolation process using the distribution coefficient preset in the setting table 19, but this embodiment In the embodiment, the missing data interpolation unit 12 performs the interpolation process using the distribution coefficient of the distribution coefficient calculation DB 23 that is updated as needed in the above process.

  The missing data interpolating means 12 determines the day of the week based on the date of the missing data and the period 1 to 4 by referring to the setting table 23c for the distribution coefficient calculation, and the distribution data of the distribution coefficient calculation DB 23 is determined. The apportioning coefficient calculation table for the corresponding period is determined from the coefficient calculating table 23b, and the apportioning coefficients of the day of the week, time, and device are extracted and used. The algorithm shown in FIGS. 5 and 6 can be used for the procedure of the interpolation process.

  As described above, according to the present embodiment, in addition to the effect of the first embodiment, there is an effect in the case of using a proportional coefficient calculated in real time as follows.

  In particular, the actual amount of energy consumed in the backyard differs depending on the day of the week (weekdays and holidays) and the time zone. For example, cook even when the holiday is later than the weekday, use different equipment depending on the cooking order, clean dishes (container) while cooking, etc. It is. Because of these characteristics, the accuracy of missing data restoration is improved by calculating and using the distribution coefficient for each day of the week (weekdays, holidays) and time in real time rather than using a fixed distribution coefficient. It becomes possible.

Next, a third embodiment will be described.
In the present embodiment, data interpolation is performed in real time, and interpolation data is corrected based on data collected thereafter, thereby realizing accurate collection management.
FIG. 15 is a functional block diagram of the data collection management device 1 according to the third embodiment.

  The data collection management device 1 according to the present embodiment is interpolated with a change rate calculation means 15 for sequentially calculating the change rate of the total energy consumption at each collection timing, with respect to the configuration of the second embodiment shown in FIG. In order to increase the reliability of the data, an interpolation data correction means 16 for correcting the interpolation data using other data collected later is added.

  Hereinafter, the operation of the data collection management device 1 according to the present embodiment will be described focusing on the difference from the second embodiment.

(Change rate calculation process)
When the change rate calculation means 15 is activated by a command from the energy consumption amount collection means 11 at each collection timing, the change rate calculation means 15 determines whether or not the total energy consumption amount has been normally collected at the collection timing and collects normally. Calculate the rate of change between the previous and current values of the total energy consumption.

  As shown in FIG. 16, the apportioning coefficient calculation table 23b according to the present embodiment also stores the rate of change of the total energy consumption calculated at each collection timing.

(Missing data interpolation process)
The operation of the missing data interpolation unit 12 according to this embodiment will be described with reference to FIG. The difference from FIG. 6 is that when all the data is missing (“No” in S508), the previous value column for the previous value of the total energy consumption using the rate of change calculated in the above process. The estimated value of the total energy consumption is calculated by applying the change rate recorded in association with (S510). At this time, the flag “2” indicating the estimated value calculated by this processing is stored in association with the estimated value. This flag indicates the reliability of data depending on the processing content.

  Further, the estimated value of the total energy consumption calculated in step S510 is multiplied by the apportioning coefficient stored in the apportioning coefficient calculation table 23b to calculate the individual energy consumption of each device (S511). The flag “2” is stored for the amount of energy consumption as described above. Other processes are the same as those in FIG.

  The display processing means 13 identifies and displays the energy consumption amount based on the flag (0 to 2).

(Interpolation data correction processing)
When the interpolation data correction means 16 is activated by an activation request from the consumption energy amount collection means 11 or the missing data interpolation means 12, the current value flag of the total consumption energy amount is “0” or “1” and the previous time. When the value flag is “2”, the previous value correction process is executed. As the contents of the correction processing, the value of the correction target data is corrected in the direction in which the change rate between the latest data of the flag “0” or “1” before and after the correction target data becomes equal.

  After the correction of the total energy consumption amount, the individual energy consumption amount of each device is calculated based on the average of the apportioning coefficient of each device of the latest data before and after the correction target used in the correction process. The corrected data is set in the flag “1”.

  For example, when the measurement data of 23:00 in the data of the energy consumption DB 22 of FIG. 18 becomes the flag 2, and then the measurement data shown in FIG. 19 is collected at the time of 24:00, the above correction is performed. The process is executed to correct the data at 23:00 in the energy consumption DB 22 as shown in FIG.

  As described above, according to the present embodiment, the reliability is given to the calculated interpolation data corresponding to the content of the interpolation processing, and the collected data is identified and displayed together with this reliability. Based on this, more appropriate evaluation and judgment are possible. In addition, the interpolation data correction means performs correction calculation on data having a reliability less than or equal to a predetermined value using data collected after that, so that highly accurate data can be stored and managed.

DESCRIPTION OF SYMBOLS 1 Data collection management apparatus 2 Sensor 11 Consumption energy amount collection means 12 Missing data interpolation means 13 Display means 14 Proportional coefficient calculation means 15 Change rate calculation means 16 Interpolation data correction means 19 Setting table 21 Measurement energy DB
22 Energy Consumption DB
23 Apportioning coefficient calculation DB
30 Display screen 50 Backyard 51 Main trunk 52 Equipment

Claims (4)

A plurality of devices are divided into one or two or more groups, and energy consumption amount collecting means for collecting the total energy consumption amount data of each group and the individual energy consumption amount data of devices belonging to each group at a predetermined cycle;
When data loss is detected in the data collected by the energy consumption collecting means, missing data interpolation means for calculating interpolation data by a predetermined interpolation process for each missing pattern;
Display means for identifying and displaying the data normally collected by the energy consumption collecting means and the interpolation data calculated by the missing data interpolation means;
Means for storing history information of energy consumption for each individual device;
Based on the history information, an apportioning coefficient calculating means for calculating an apportioning coefficient for the total energy consumption of the individual energy consumption of each device;
A rate of change calculation means for calculating the rate of change of the previous value and the current value of the total energy consumption at each collection timing ;
With
When the missing data interpolation means detects the missing data of all the data of the same group at a certain collection timing, the latest change rate of the change rates calculated by the change rate calculation means is used. calculating a total consumed energy, the total consumed energy in characteristics and to Lud over data collection and management system to calculate the individual energy consumption of applying the proration factor of each device belonging to the group the devices . The missing data interpolation means adds reliability corresponding to the interpolation processing to the calculated interpolation data,
The data collection management device according to claim 1 , wherein the display means identifies and displays data based on the reliability added to the interpolation data when the interpolation data is identified and displayed. When the reliability added to the interpolated data calculated by the missing data interpolating means is less than or equal to a predetermined value, an interpolating data correcting means for correcting the interpolated data using data collected at a later acquisition timing is provided. The data collection management device according to claim 2 . A deficient data interpolation method that collects energy consumption data of a plurality of devices, detects deficits in data, and interpolates deficient data,
Dividing a plurality of devices into one or more groups and collecting the total energy consumption data of each group and the individual energy consumption data of devices belonging to each group at a predetermined period;
A step of storing energy consumption history information for each individual device;
Based on the history information, calculating a distribution coefficient for the total energy consumption of the individual energy consumption of each device;
Calculating the rate of change between the previous and current values of total energy consumption at each collection timing;
When data missing is detected in the collected data, a predetermined interpolation process is executed for each missing pattern, while when the data missing of all data in the same group is detected at a certain collection timing, the above calculation is performed. After calculating the total energy consumption of the group using the latest rate of change, the individual consumption of each device is applied to the total energy consumption by applying the apportioning coefficient of each device belonging to the group. Executing an interpolation process for calculating an energy amount;
Identifying and displaying normally collected data and data calculated by the interpolation process;
A method for interpolating missing data, comprising: