WO2024047706A1

WO2024047706A1 - Management device, management system, and management method

Info

Publication number: WO2024047706A1
Application number: PCT/JP2022/032434
Authority: WO
Inventors: 冬樹佐藤; 宣明田崎; 玄太吉村
Original assignee: 三菱電機ビルソリューションズ株式会社; 三菱電機株式会社
Priority date: 2022-08-29
Filing date: 2022-08-29
Publication date: 2024-03-07
Also published as: JP7433564B1

Abstract

This management device comprises: an acquisition unit for acquiring operation history data for a facility apparatus in a prescribed period; and a classification processing unit for classifying operation patterns of the facility apparatus in the prescribed period on the basis of the operation history data acquired by the acquisition unit. The classification processing unit is provided with: an operation pattern calculation unit that calculates a first operation pattern for a first period and a second operation pattern for a remaining second period, the first and second periods being obtained when the prescribed period is divided into unit periods; a change calculation unit that calculates the degree of change in the operation patterns using the first operation pattern and the second operation pattern when the prescribed period is divided into the unit periods; a dividing point calculation unit that calculates, as a dividing point, a point in the prescribed period at which the degree of change satisfies a prescribed condition; and a division processing unit that divides the prescribed period at the calculated dividing point.

Description

Management device, management system and management method

The present disclosure relates to a management device, a management system, and a management method for managing power in facilities such as buildings.

Large-scale facilities such as buildings and factories use heat source systems that collectively generate and supply cold and hot water, or heat, for use in air conditioning systems by operating multiple equipment that serves as heat sources.

In response to the growing awareness of energy conservation, equipment control systems that control the operation of multiple pieces of equipment are required to discretely start and stop these equipment depending on the load of the facility in order to operate these equipment efficiently. They are designed to operate independently or in parallel.

The operation patterns of these equipment are often determined based on the operator's experience and intuition. For this reason, it is difficult to objectively judge what kind of operation pattern is actually efficient, and it has not been possible to easily identify an operation pattern that can improve the operation of equipment.

In this regard, for example, Japanese Utility Model Application Publication No. 6-30838 discloses a method of outputting an operation pattern code for each piece of equipment over a certain period of time.

Utility Model Publication No. 6-30838

However, the above publication merely outputs an operation pattern code, and does not analyze the characteristics of the operation pattern of each piece of equipment.

The present disclosure has been made to solve the above problems, and provides a management device, a management system, and a management method that can easily identify the operation pattern of equipment.

A management device according to an embodiment includes an acquisition unit that acquires operation history data for equipment in a predetermined period, and a classification processing unit that classifies operation patterns of equipment in a predetermined period based on the operation history data acquired by the acquisition unit. Equipped with. The classification processing unit includes an aggregation unit that aggregates daily or hourly operation information based on operation history data, and a feature amount calculation unit that calculates a feature amount that characterizes the operation pattern of equipment in a predetermined period based on the operation information. , a determination unit that compares the calculated feature amount with a predetermined threshold value and classifies the operation pattern based on the comparison result.

A management system according to an embodiment includes an acquisition unit that acquires operation history data for equipment over a predetermined period, and a classification processing unit that classifies operation patterns of equipment over a predetermined period based on the operation history data acquired by the acquisition unit. Equipped with. The classification processing unit includes an aggregation unit that aggregates daily or hourly operation information based on operation history data, a feature calculation unit that calculates feature quantities that characterize the operating pattern of equipment in a predetermined period, and a and a determination unit that compares the amount with a predetermined threshold and classifies the operation pattern based on the comparison result.

A management method according to an embodiment includes the steps of acquiring operation history data for equipment over a predetermined period, and classifying operation patterns of the equipment over a predetermined period based on the acquired operation history data. The step of classifying operation patterns includes the step of aggregating daily or hourly operation information based on operation history data, and calculating feature quantities that characterize the operation patterns of equipment over a predetermined period based on the aggregated operation information. and a step of comparing the calculated feature amount with a predetermined threshold value and classifying the operation pattern based on the comparison result.

A management device, a management system, and a management method according to an embodiment can easily identify operation patterns of equipment.

These and other objects, features, aspects, and advantages of this disclosure will be understood in conjunction with the accompanying drawings and will become apparent from the following detailed description of this disclosure.

FIG. 1 is a diagram illustrating a conceptual diagram of a management system 1 according to an embodiment. FIG. 2 is a diagram illustrating functional blocks of a management device 100 according to an embodiment. FIG. 3 is a flow diagram illustrating classification processing of the management device 100 according to an embodiment. FIG. 2 is a diagram illustrating daily operation information of equipment according to an embodiment. It is a figure explaining operation information for each time of the equipment according to a certain embodiment. FIG. 3 is a diagram illustrating calculation of a feature amount by a feature amount calculation unit 128 according to an embodiment. FIG. 3 is a diagram illustrating processing of a display control unit 129 according to an embodiment. It is a figure explaining the functional block of management device 100 according to modification 1 of a certain embodiment. It is a figure explaining daily operation information of equipment according to modification 1 of a certain embodiment.

Hereinafter, embodiments will be described based on the drawings. In the following description, the same parts are given the same reference numerals. Since their names and functions are also the same, detailed explanations thereof will not be repeated.

<A. System configuration>
FIG. 1 is a diagram illustrating a conceptual diagram of a management system 1 according to an embodiment. Referring to FIG. 1, a management system 1 includes equipment of a facility such as a building, and a management device 100 that manages the equipment. Examples of the equipment 2 include sensors, air conditioners, lighting, fans, and the like. Note that the equipment is not limited to this, and may be any equipment that consumes power used in the facility.

The management device 100 includes a CPU (Central Processing Unit) 12, a storage section 20, a main memory 18, an input section 10, a display section 14, and a network communication section 16.

The storage unit 20 is a device that stores information, and stores various programs, data, and the like. The storage unit 20 stores operation history data 110 for equipment.

The main memory 18 is a working memory such as DRAM (Dynamic Random Access Memory). The input unit 10 is a keyboard, a mouse, etc., and is used by the user to perform operations. The input unit 10 may include an interface device that accepts data input from other systems. The display unit 14 is a display, and may be a liquid crystal display, an organic EL (Electro Luminescence) display, or the like. The CPU 12 implements various functions by executing programs stored in the storage unit 20. In other aspects, each function may be realized by a circuit element or other hardware configured to realize the function. The network communication unit 16 is provided to be able to communicate with other devices via a network.

FIG. 2 is a diagram illustrating functional blocks of the management device 100 according to an embodiment. Referring to FIG. 2, CPU 12 includes an acquisition section 120, a classification processing section 121, and a display control section 129. The classification processing unit 121 includes a totaling unit 122, a determining unit 126, a feature calculating unit 128, and a threshold setting unit 127.

The acquisition unit 120 acquires the operation history data 110 stored in the storage unit 20.
The classification processing unit 121 classifies the operation patterns of equipment in a predetermined period based on the operation history data 110 acquired by the acquisition unit 120.

The aggregation unit 122 aggregates operation information on a daily or hourly basis based on operation history data 110 of equipment during a predetermined period.

The aggregation unit 122 includes a day pattern aggregation unit 124 and a time pattern aggregation unit 125.
The daily pattern aggregation unit 124 aggregates daily operation information based on the operation history data 110 of equipment during a predetermined period.

The time pattern aggregation unit 125 aggregates operation information for each time based on the operation history data 110 of equipment during a predetermined period.

The feature amount calculation unit 128 calculates a feature amount that characterizes the operation pattern of the equipment in a predetermined period based on the operation information.

The determination unit 126 compares the calculated feature amount with a predetermined threshold, and classifies the operation pattern based on the comparison result.

The threshold setting unit 127 sets the threshold value used by the determination unit 126 according to the user's instructions.

The display control unit 129 displays the classification results of the operating patterns of the equipment on the display unit 14.
<B. Processing flow＞
FIG. 3 is a flow diagram illustrating classification processing of the management device 100 according to an embodiment.

Referring to FIG. 3, the acquisition unit 120 acquires operation history data of the equipment for a predetermined period (step S2). The equipment may be designated by the user from among a plurality of equipment, or may be designated one by one from among the plurality of equipment. Regarding the predetermined period, the user may specify an arbitrary period, or may specify the period in units of years or months. In this example, a case of one year will be explained.

Next, the daily pattern aggregation unit 124 aggregates daily operation information based on the operation history data of the equipment in a predetermined period (step S4).

Next, the time pattern aggregation unit 125 aggregates the operation information for each time based on the operation history data of the equipment in a predetermined period (step S6).

Next, the feature amount calculation unit 128 calculates a feature amount that characterizes the operation pattern of the equipment in a predetermined period based on the operation information (step S8).

Next, the determination unit 126 compares the calculated feature amount with a predetermined threshold, and classifies the operation pattern based on the comparison result (step S10).

Next, the display control unit 129 displays the classification results of the operating patterns of the equipment on the display unit 14 (step S12).

Then, the process ends.
By executing the above process for each of a plurality of pieces of equipment, it becomes possible to display a list of the classification results of the operation patterns of the plurality of pieces of equipment.

<C. Operation information＞
FIG. 4 is a diagram illustrating daily operation information of equipment according to an embodiment.

Referring to FIG. 4, three types of operation information compiled by the daily pattern aggregation unit 124 based on the operation history data 110 are shown. As an example, the equipment will be explained using an air conditioner as an example.

FIG. 4(A) shows the daily operating time ratio during the predetermined period (January 1, 2018 to December 31, 2018). Here, the number of working days during the predetermined period is 250 days, and the average number of working days per week is 4.79 days. Further, a case is shown in which the average operating time ratio is 39.74%.

FIG. 4(B) shows the value of the number of times the power is turned on and turned off for each day during the predetermined period (January 1, 2018 to December 31, 2018). The average number of times the power is turned on per day is 1.144 times. Further, the average number of times the power is turned off per day is 1.144 times.

FIG. 4(C) shows the number of changes in the set temperature for each day during the predetermined period (January 1, 2018 to December 31, 2018). The average number of changes in the set temperature per day is 1.388 times.

FIG. 5 is a diagram illustrating operation information for each time of equipment according to an embodiment.

Referring to FIG. 5, operation information aggregated and averaged by time pattern aggregation unit 125 based on operation history data 110 is shown. As an example, the equipment will be explained using an air conditioner as an example.

In this example, the averaged operating rate for each 24-hour period during the predetermined period (January 1, 2018 to December 31, 2018) is shown.

<D. Specific example of operation pattern>
<d1. Day of the week operation pattern＞
Based on the daily operation information aggregated by the daily pattern aggregation unit 124, the equipment is classified as an operation pattern for each day of the week in one week.

In this example, the operation pattern is classified as operating on each day of the week based on the ratio of the number of operating days of the equipment on each day of the week.

Specifically, the feature quantity calculation unit 128 calculates the percentage of working days for each day of the week in a predetermined period (January 1, 2018 to December 31, 2018) based on the operating information explained in FIG. 4(A). is calculated as the feature quantity of the equipment.

The determination unit 126 compares the calculated feature amount with a predetermined threshold value, and based on the comparison result, classifies the equipment as an operation pattern in which it is operated on the corresponding day of the week.

Specifically, the determination unit 126 compares the calculated feature amount with a predetermined threshold value (50%), and if it is determined that the calculated feature value is equal to or greater than the predetermined threshold value, the determination unit 126 determines whether the equipment is operated on the corresponding day of the week. It is classified as an operating pattern.

For example, the ratio of the average number of working days on Mondays during a predetermined period (January 1, 2018 to December 31, 2018) is calculated as a feature quantity, and compared with a predetermined threshold (50%). If it is determined that the ratio of the average number of working days on Monday is equal to or higher than a predetermined threshold (50%), the equipment is classified as an operating pattern in which it is operated on Mondays. It is possible to classify other days of the week according to the same method.

<d2. Operation pattern of overnight operation or stop>
Based on the daily operation information compiled by the daily pattern tabulation unit 124, the equipment is classified as an operation pattern of all-night operation or all-night stop.

In this example, equipment is classified as an operating pattern in which it operates all night or is stopped all night based on the daily average operating time ratio.

Specifically, the feature value calculation unit 128 calculates the daily average operating time ratio during a predetermined period (January 1, 2018 to December 31, 2018) based on the operating information explained in FIG. 4(A). is calculated as the feature quantity of the equipment.

The determination unit 126 compares the calculated feature amount with a predetermined threshold value (99%), and classifies the equipment as an operation pattern in which it is operated all night based on the comparison result.

Specifically, the determination unit 126 compares the calculated feature amount with a predetermined threshold value (99%), and if it is determined that the calculated feature value is equal to or greater than the predetermined threshold value, the determination unit 126 determines that the equipment is in an operating pattern in which the equipment is operated all night. Classify as.

The determination unit 126 compares the calculated feature amount with a predetermined threshold value (1%), and based on the comparison result, classifies the equipment as an operating pattern in which it is stopped all night.

Specifically, the determination unit 126 compares the calculated feature amount with a predetermined threshold value (1%), and if it is determined that the calculated feature value is less than the predetermined threshold value, the determination unit 126 determines that the equipment is in an operation pattern in which it is stopped all night. Classify as.

For example, for a certain piece of equipment, the daily average operating time ratio (39.74%) during a predetermined period (January 1, 2018 to December 31, 2018) is calculated as a feature quantity, and a predetermined threshold value ( 99% or 1%). In this case, the equipment is classified as not having an operating pattern of operating all night or stopping all night. It is possible to classify other equipment in the same way.

<d3. Operation pattern regarding number of operations>
Based on the daily operation information compiled by the daily pattern aggregation unit 124, it is classified as an operation pattern related to the number of operations of the equipment.

In this example, equipment is classified as an operation pattern related to the number of operations based on the number of operations per day (power on/off times).

Specifically, the feature value calculation unit 128 calculates the daily average power-on/time during a predetermined period (January 1, 2018 to December 31, 2018) based on the operation information explained in FIG. 4(B). Calculate the number of times the power is turned off as a feature of the equipment.

The determination unit 126 compares the calculated feature amount with a predetermined threshold value (3 times), and classifies the equipment as an operation pattern related to the number of operations based on the comparison result.

Specifically, the determination unit 126 compares the calculated feature amount with a predetermined threshold value (3 times), and if it is determined that the calculated feature value is equal to or greater than the predetermined threshold value, the determination unit 126 determines that the number of times the power is turned on/off is large for the equipment. Classified as an operating pattern.

For example, for a certain piece of equipment, the average number of times the power is turned on per day (1.144 times/day) during a predetermined period (January 1, 2018 to December 31, 2018) is calculated as a feature quantity, and a predetermined Compare with the threshold (3 times). In this case, the equipment is classified as not having an operation pattern with a large number of power on/off times. It is possible to classify other equipment in the same way.

Note that in this example, a case has been described in which the number of times the power is turned on and the number of times the power is turned off are respectively counted, but either one of them may be counted.

<d4. Operation pattern regarding the number of operations for the set temperature>
Based on the daily operation information compiled by the daily pattern aggregation unit 124, the operation pattern is classified as an operation pattern related to the number of times the set temperature of the equipment is operated.

In this example, equipment is classified as an operation pattern related to the number of set temperature operations based on the number of daily set temperature operations (number of set temperature changes).

Specifically, the feature value calculation unit 128 changes the average set temperature for each day during a predetermined period (January 1, 2018 to December 31, 2018) based on the operation information explained in FIG. 4(C). The number of times is calculated as the feature quantity of the equipment.

The determination unit 126 compares the calculated feature amount with a predetermined threshold value (3 times), and classifies the equipment as an operation pattern related to the number of times the set temperature is operated based on the comparison result.

Specifically, the determination unit 126 compares the calculated feature amount with a predetermined threshold value (three times), and if it is determined that the feature value is equal to or greater than the predetermined threshold value, the determination unit 126 determines that the number of times the set temperature has been operated for the equipment is large. Classified as an operating pattern.

For example, for a certain piece of equipment, the average number of daily set temperature changes (1.388 times/day) during a predetermined period (January 1, 2018 to December 31, 2018) is calculated as a feature quantity, and Compare with the threshold value (3 times). In this case, the equipment is classified as not having an operation pattern in which the set temperature is operated many times. It is possible to classify other equipment in the same way.

<d5. Operation patterns related to routine operations>
Based on the averaged operation information for each time aggregated by the time pattern aggregation unit 125, it is classified as an operation pattern related to the routine operation of equipment.

In this example, equipment is classified as an operation pattern related to routine operations based on the average operation rate for each time.

Specifically, the feature amount calculation unit 128 calculates the average absolute error in a predetermined period (January 1, 2018 to December 31, 2018) as the feature amount of the equipment based on the operation information explained in FIG. calculate.

FIG. 6 is a diagram illustrating feature amount calculation by the feature amount calculation unit 128 according to an embodiment.

FIG. 6(A) shows the operating information explained in FIG. 5.
The feature amount calculation unit 128 generates normalized data based on the operation information by dividing the operation rate at each time by the maximum value (in the range of 0 to 1), as shown by the solid line in FIG. 6(B). do.

Furthermore, the feature amount calculation unit 128 generates binarized data based on the generated normalized data as shown by the dotted line in FIG. 6(B). Specifically, the feature value calculation unit 128 sets a binary value to "1" if the value of the generated normalized data is 0.5 or more, and sets it to "0" if the normalized data is less than 0.5. Generate converted data.

The determination unit 126 calculates the average absolute error between the normalized data and the binarized data as a feature quantity, and classifies it as an operation pattern related to routine operation based on the comparison result.

Specifically, the determination unit 126 compares the calculated feature amount with a predetermined threshold value (30), and classifies the equipment as an operation pattern related to routine operation based on the comparison result.

Specifically, the determination unit 126 compares the calculated feature amount with a predetermined threshold value (30), and if it is determined that it is less than the predetermined threshold value, the determination unit 126 classifies the equipment as an operation pattern related to routine operation. . It is possible to classify other equipment in the same way.

<d6. Operation pattern related to forgetting to turn off operation>
Based on the averaged operation information for each time aggregated by the time pattern aggregation unit 125, the operation pattern is classified as an operation pattern related to an operation of forgetting to turn off equipment.

In this example, equipment is classified as an operation pattern related to forgetting to turn off operations based on the average operation rate for each time.

The determination unit 126 determines the average of the normalized data and the binarized data in the time period from when the binarized data shown in FIG. 6B changes from 1 to 0 until the normalized data takes the minimum value. The absolute error is calculated as a feature quantity, and based on the comparison result, it is classified as an operation pattern related to forgetting to turn off the data.

Specifically, the determination unit 126 compares the calculated feature amount with a predetermined threshold value (10), and classifies the equipment as an operation pattern related to a forgetting operation based on the comparison result.

Specifically, the determination unit 126 compares the calculated feature amount with a predetermined threshold value (10), and if it is determined that the feature value is equal to or greater than the predetermined threshold value, the determination unit 126 classifies the equipment as an operation pattern related to forgetting to turn off the device. do. It is possible to classify other equipment in the same way.

<d7. Operation pattern related to scheduled operation>
Based on the averaged operation information for each time aggregated by the time pattern aggregation unit 125, it is classified as an operation pattern related to scheduled operation of equipment.

In this example, equipment is classified as an operation pattern related to scheduled operation based on the average operation rate for each time.

Specifically, the feature quantity calculation unit 128 calculates the difference in the normalized data of the operating information for the predetermined period (January 1, 2018 to December 31, 2018) explained in FIG. 6(B) of the equipment. Calculate as a feature quantity.

More specifically, the feature value calculation unit 128 calculates the difference between the normalized data of the operation information and the normalized data of 10 minutes ago as the feature value of the equipment, as shown in FIG. 6(C). .

The determination unit 126 compares the calculated feature amount, which is the difference data shown in FIG. 6(C), with a predetermined threshold value, and classifies the equipment as an operation pattern related to scheduled operation based on the comparison result.

The determination unit 126 compares the calculated maximum value of the feature amount with a predetermined threshold value (0.5), and classifies the equipment as an operation pattern based on the start of scheduled operation based on the comparison result.

Specifically, the determination unit 126 compares the calculated maximum value of the feature amount with a predetermined threshold value (0.5), and if it is determined that the maximum value of the calculated feature value is equal to or greater than the predetermined threshold value, the determination unit 126 performs scheduled operation for the equipment. Classified as an operation pattern based on start.

The determination unit 126 compares the calculated minimum value of the feature amount with a predetermined threshold value (-0.5), and based on the comparison result, classifies the equipment as an operation pattern due to the end of scheduled operation.

Specifically, the determination unit 126 compares the minimum value of the calculated feature amount with a predetermined threshold (-0.5), and if it is determined that the minimum value is less than the predetermined threshold, schedule operation is performed for the equipment. It is classified as an operation pattern due to the end of the period.

For example, for a certain piece of equipment, the maximum value (0.9038) and minimum value (-0.8846) of the difference in normalized data during a predetermined period (January 1, 2018 to December 31, 2018) are the feature values. It is calculated as follows and compared with a predetermined threshold value (0.5 or -0.5). In this case, the equipment is classified as having an operation pattern of start of scheduled operation and end of scheduled operation. It is possible to classify other equipment in the same way.

<E. Classification results>
FIG. 7 is a diagram illustrating processing of the display control unit 129 according to an embodiment.

Referring to FIG. 7, a classification result screen displayed on the display unit 14 is shown. The display control unit 129 displays the classification results of the operating patterns of the equipment classified by the classification processing unit 121.

As an example, the classification result screen shows the operating status of each piece of equipment during a predetermined period (January 1, 2018 to December 31, 2018). As an example, equipment numbers 0 to 24 are assigned to each of a plurality of equipment.

Information regarding the operation pattern of each equipment is shown.
Specifically, "all-night operation", "all-night stop", "many number of temperature setting operations", "many power on/off times", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday" ”, “Saturday”, “Sunday”, “routine operation”, “scheduled operation start”, “scheduled operation end”, and “forgot to turn off operation” information (True/False) is shown.

By displaying a list of information (True/False) regarding the operation pattern of each piece of equipment, it is possible to easily identify the operation pattern of each piece of equipment. It can be used for management work of buildings, etc.

(Modification 1)
In the above, a method has been described in which the user specifies a predetermined period and classifies the operation pattern of each equipment during the specified period.

On the other hand, since operating patterns have different characteristics for each period, the operating patterns may be classified by dividing into each period with characteristics.

FIG. 8 is a diagram illustrating functional blocks of the management device 100 according to Modification 1 of an embodiment. Referring to FIG. 8, this example differs in that CPU12 is changed to CPU12#. CPU 12# differs from CPU 12 in that classification processing section 121 is replaced with classification processing section 121#. The classification processing section 121# differs from the classification processing section 121 in that a period division section 123 is further added. Since the other configurations are the same as those in FIG. 2, detailed description thereof will not be repeated.

The period division unit 123 divides a predetermined period based on the operation history data acquired by the acquisition unit 120.

FIG. 9 is a diagram illustrating daily operation information of equipment according to Modification 1 of an embodiment.

Referring to FIG. 9, FIGS. 9(A) to 9(C) are similar to those described in FIGS. 4(A) to 4(C). FIG. 9(D) shows the daily rate of operation modes calculated by the daily pattern calculation unit 124 based on the operation history data 110. Here, the percentage of operation modes such as heating and cooling is shown by the type of hatching.

As an example, the period dividing unit 123 divides the predetermined period based on the ratio of the operation modes of the operation history data acquired by the acquisition unit 120.

As an example, the period dividing unit 123 divides the predetermined period into a heating period, a cooling period, and an intermediate period based on the ratio of the operation modes.

The classification processing unit 121# classifies the operating patterns of equipment in each divided period.

Other processing is the same as described above.
This processing makes it possible to classify operating patterns according to the actual situation, and can be used for management work of buildings, etc.

Note that in this example, a case has been described in which the predetermined period is divided using the ratio of the driving modes, but the predetermined period is not limited to this, and the predetermined period may be divided using other methods.

For example, the predetermined period may be divided depending on the set temperature range, or the predetermined period may be divided into a period with a high operating rate and a period with a low operating rate.

Alternatively, a change in the operating pattern may be detected and the predetermined period may be divided based on the change.

(Modification 2)
In the above, a case has been described in which the threshold value setting unit 127 sets the threshold value used by the determination unit 126 in accordance with the user's instructions. For example, a slider that can adjust the threshold value may be provided, and the user may directly operate the slider to set the threshold value.

Further, the threshold setting unit 127 may automatically adjust the threshold value.
For example, the information (True/False) on the classification result screen explained in FIG. 7 can be changed by the user through an instruction operation. By changing the information, the threshold setting unit 127 may automatically adjust the threshold value so that the classification result matches the change.

(Modification 3)
In FIG. 7 described above, a case has been described in which the classification results of operation patterns related to equipment are displayed, but the present invention is not limited to this, and information related to other equipment may also be displayed.

For example, as information related to the equipment, information such as power consumption, specifications, installation date, room area, etc. of the equipment may be displayed in association with each other.

Through this process, the user can easily check various information regarding the equipment in a list.

(Modification 4)
The display is not limited to the display shown in FIG. 7, and a filter function or a sort function may be provided.

Specifically, as a filter function, it may be possible to narrow down the equipment based on conditions such as the presence or absence of features. Alternatively, as a sorting function, it may be possible to rearrange the equipment in descending or ascending order of power consumption, index, or the like.

By providing a filter function or a sort function, it is possible to easily narrow down or rearrange a large list of equipment. This allows the user to easily refer to information on the equipment of interest.

(Modification 5)
The display is not limited to the above-described display in FIG. 7, and data on which the classification determination of the operation pattern of the equipment is based may be displayed.

Specifically, the data explained in FIGS. 4 to 6, etc. may be displayed as the basis for classification determination. Alternatively, the distribution of threshold values for the entire equipment, the threshold values of selected equipment, etc. may be displayed. This display allows the user to easily confirm the validity of the basis for determining the classification of the operating pattern.

<F. Others＞
Although the above-mentioned embodiment explained the configuration in which the operation history data was stored in advance in the storage unit 20, the configuration is not limited to this, and for example, the server may store the operation history data.

Note that the methods described in each of the above embodiments are applicable to magnetic disks (hard disks, etc.), optical disks (CD-ROM (Compact Disc-Read Only Memory), DVD (Digital Versatile Disc)) as programs that can be executed by a computer. ), magneto-optical disks, semiconductor memories, and other storage media for distribution. Further, the storage medium may be in any storage format as long as it can store a program and is readable by a computer.

In addition, the operating system, database management software, network software, and other middleware running on the computer based on the instructions of the program installed on the computer from the storage medium are part of each process to realize the above embodiments. may be executed. Furthermore, the storage medium in each embodiment is not limited to a medium independent of a computer, but also includes a storage medium in which a program transmitted via a LAN (Local Area Network), the Internet, etc. is downloaded and stored or temporarily stored. Further, the number of storage media is not limited to one, and cases in which the processing in each of the above embodiments is executed from a plurality of media are also included in the storage medium of the present disclosure, and the media configuration may be any configuration. Note that the computer in each embodiment executes each process in each of the above embodiments based on a program stored in a storage medium, and includes a single device such as a personal computer, or a plurality of devices connected to a network. Any configuration of connected systems etc. may be used.

The embodiment disclosed this time is an example and is not limited to the above content. The scope of the present disclosure is indicated by the claims, and it is intended that all changes within the meaning and range equivalent to the claims are included.

1 Management system, 2 Equipment equipment, 10 Input unit, 14 Display unit, 16 Network communication unit, 18 Main memory, 20 Storage unit, 100 Management device, 110 Operation history data, 120 Acquisition unit, 121 Classification processing unit, 122 Aggregation unit , 123 Period division section, 124 Day pattern aggregation section, 125 Time pattern aggregation section, 126 Judgment section, 127 Threshold value setting section, 128 Feature value calculation section, 129 Display control section.

Claims

an acquisition unit that acquires operation history data for equipment during a predetermined period;
a classification processing unit that classifies the operation pattern of the equipment during the predetermined period based on the operation history data acquired by the acquisition unit;
The classification processing unit is
an aggregation unit that aggregates daily or hourly operating information based on the operating history data;
a feature amount calculation unit that calculates a feature amount characterizing the operation pattern of the equipment in the predetermined period based on the operation information;
A management device comprising: a determination unit that compares the calculated feature amount with a predetermined threshold value and classifies an operation pattern based on the comparison result.
The feature amount calculation unit calculates a ratio of the number of working days for each day of the week in the predetermined period as a feature amount based on the operation information,
The management device according to claim 1, wherein the determination unit compares the calculated feature amount with the predetermined threshold value, and classifies the operation pattern as one in which driving is performed on a corresponding day of the week based on the comparison result.
The feature amount calculation unit calculates a daily average operating time ratio in the predetermined period as a feature amount based on the operation information,
The management device according to claim 1, wherein the determination unit compares the calculated feature amount with the predetermined threshold value and classifies the daily operation pattern in the predetermined period based on the comparison result.
The determination unit includes:
The calculated feature amount is compared with a first threshold value, and if it is determined that the calculated feature amount is greater than or equal to the first threshold value, the operation pattern is classified as an all-night operation mode operation pattern. ,
The calculated feature amount is compared with a second threshold value, and if it is determined that the calculated feature amount is less than the second threshold value, the operation pattern is that the operation is stopped all night. The management device according to claim 3, which is classified as:
The feature amount calculation unit calculates the number of operations per day in the predetermined period as a feature amount based on the operation information,
The management device according to claim 1, wherein the determination unit compares the calculated feature amount with the predetermined threshold value and classifies the operation pattern regarding the number of daily operations in the predetermined period based on the comparison result.
The determination unit compares the calculated feature amount with a first threshold value, and if it is determined that the calculated feature amount is equal to or greater than the first threshold value, the determination unit determines that the operation pattern has a large number of operations. The management device according to claim 1, which is classified as:
The feature amount calculation unit calculates the number of operations related to the daily set temperature in the predetermined period as a feature amount based on the operation information,
The determining unit compares the calculated feature amount with the predetermined threshold value, and classifies the operation pattern regarding the number of daily set temperature operations in the predetermined period based on the comparison result. Management device.
The determination unit compares the calculated feature amount with a first threshold, and if it is determined that the calculated feature amount is greater than or equal to the first threshold, the determination unit determines the number of operations related to the set temperature. The management device according to claim 7, which classifies the management device as a frequent operation pattern.
The feature amount calculation unit calculates the operating rate for each time as a feature amount based on the operation information,
The management device according to claim 1, wherein the determination unit compares the calculated feature amount and the predetermined threshold value, and classifies the operation pattern as a time-based operation pattern based on the comparison result.
The feature amount calculation unit is
Generate first data that normalizes the operating rate for each time,
generating second data by binarizing the first data;
Calculating the average absolute error of the first data and the second data as a feature amount,
The management device according to claim 9, wherein the determination unit compares the calculated feature amount with the predetermined threshold value and classifies the operation pattern based on the comparison result.
When the determination unit determines that the average absolute error of the first and second data, which is the feature amount, is less than a first predetermined value, the determination unit classifies the operation pattern as an operation pattern due to routine operation. management device.
10. When the determination unit determines that the average absolute error of the first and second data, which is the feature amount, is equal to or greater than a second predetermined value, the determination unit classifies the operation pattern as an operation pattern of forgetting to turn off. Management device as described.
When the determination unit determines that the maximum value of the average absolute error of the first and second data, which is the feature quantity, is equal to or greater than a third predetermined value, the determination unit classifies the operation pattern as an operation pattern due to the start of scheduled operation. 11. The management device according to claim 10.
When the determination unit determines that the minimum value of the average absolute error of the first and second data, which is the feature amount, is less than a fourth predetermined value, the determination unit classifies the operation pattern as an operation pattern due to the end of scheduled operation. 11. The management device according to claim 10.
The management device according to claim 1, further comprising a threshold setting unit that adjusts the predetermined threshold according to a user operation.
The management device according to claim 1, further comprising a display control unit that displays classification results of operation patterns of the equipment.
further comprising a period division unit that divides the predetermined period based on the operation history data acquired by the acquisition unit,
The management device according to claim 1, wherein the classification processing unit classifies operation patterns of equipment in divided periods obtained by dividing the predetermined period based on the operation history data acquired by the acquisition unit.
an acquisition unit that acquires operation history data for equipment during a predetermined period;
a classification processing unit that classifies the operation pattern of the equipment during the predetermined period based on the operation history data acquired by the acquisition unit;
The classification processing unit is
an aggregation unit that aggregates daily or hourly operating information based on the operating history data;
a feature amount calculation unit that calculates a feature amount that characterizes the operation pattern of the equipment during the predetermined period;
A management system comprising: a determination unit that compares the calculated feature amount with a predetermined threshold value and classifies an operation pattern based on the comparison result.
a step of acquiring operation history data for the equipment over a predetermined period;
a step of classifying the operation pattern of the equipment during the predetermined period based on the acquired operation history data,
The step of classifying the operation pattern includes:
aggregating daily or hourly operating information based on the operating history data;
calculating a feature amount characterizing the operation pattern of the equipment in the predetermined period based on the aggregated operation information;
A management method comprising the step of comparing the calculated feature amount with a predetermined threshold value and classifying operation patterns based on the comparison result.