JP2011048798A - Information generation system, information generation method, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To generate information useful for an owner and a tenant of a rental such as rental office, rental building. <P>SOLUTION: A contract management unit 5 manages the data regarding use contract of the rental. A data acquisition unit 1 acquires various data regarding the use state of an electrical facility of the rental. A log data recording unit 3 records as time series data the acquired data arranged in a time series manner. A matching degree calculation unit 4 successively calculates the matching degree when the recent time series data for a certain period of time is overlapped with the past time series data before the former time series data, with shifting the overlapping time points. A specifying unit 6 specifies the overlapping time point with the highest matching degree calculated by the matching degree calculation unit 4. A display control unit 7 controls the display contents including the time series data for the certain period of time using the specified time series as reference according to the contract contents managed by the contract content management unit 5. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to an information generation system, an information generation method, and a program for generating information useful for owners and tenants of rental properties such as rental offices and rental buildings.

  Conventionally, a system for monitoring power consumption has been disclosed in order to achieve energy saving (see, for example, Patent Document 1). In order to raise awareness of energy saving to the user who uses the electrical equipment as in this system, it is effective to clearly indicate the amount of power used to the user.

  In recent years, with the growing awareness of energy conservation, energy conservation measures for rental properties such as rental offices and rental buildings, along with location conditions and rents, are important points in choosing rental properties. ing.

JP 2006-309325 A

  However, when selecting a rental property, a system that can specifically evaluate the rental property using the realization of energy saving measures in the rental property as a selling point has not yet been constructed.

  The present invention has been made in view of the above circumstances, and provides an information generation system, an information generation method, and a program capable of generating useful information for owners and tenants of rental properties such as rental offices and rental buildings. With the goal.

  In order to achieve the above object, in the information generation system according to the present invention, the contract content management unit manages the contract content of the rental property. The data acquisition unit acquires data on the usage status of the electrical equipment of the rental property. The recording unit records the data acquired by the data acquisition unit in time series and records it as time series data. The degree-of-match calculation unit superimposes the first time-series data of the most recent period among the time-series data recorded in the recording unit on the second time-series data that is earlier than the first time-series data. The degree of coincidence is calculated sequentially while shifting the overlapping time point. The specifying unit specifies the overlapping time point at which the matching degree calculated by the matching degree calculating unit is the highest. The display control unit controls display content including time-series data of a certain period with the specified time as a reference, out of the second time-series data, in accordance with the contract content managed by the contract content management unit.

  According to the present invention, the time-series data for a certain period based on the past time point in the situation closest to the current usage status of the rental property can be regarded as the estimation data of the usage status of the rental property at the present time. it can. Therefore, if the display content including this time-series data is displayed while being controlled according to the contract contents of the rental property, this time-series data can be effectively used as useful information for energy saving of the rental property. Can do.

It is a block diagram which shows the structure of the information generation system which concerns on embodiment of this invention. It is a block diagram for demonstrating the use environment of the information generation system of FIG. 3A is an example of time-series data recorded in the power log data recording unit of FIG. 1, and FIG. 3B shows a time-series data comparison method in the power match degree calculation unit of FIG. It is a figure for demonstrating. It is a flowchart of the analysis process of the information generation system of FIG. It is a flowchart of the process of a phase 1. It is an example of present data. It is an example (the 1) of the representative value vector of present data. It is an example (the 1) of the fluctuation value vector of present data. It is an example (the 2) of the representative value vector of present data. It is an example (the 2) of the fluctuation value vector of present data. It is an example (the 3) of the representative value vector of present data. It is an example (the 3) of the fluctuation value vector of present data. It is an example of log data. It is an example (the 1) of the representative value vector of log data. It is an example (the 1) of the fluctuation value vector of log data. It is an example (the 2) of the representative value vector of log data. It is an example (the 2) of the fluctuation value vector of log data. It is an example (the 3) of the representative value vector of log data. It is an example (the 3) of the fluctuation value vector of log data. It is a table for demonstrating the calculation formula of an integrated value. It is a flowchart of the process of a phase 2. It is an example of the comparison display of present data and log data. It is a flowchart of the process of a phase 3. 10 is a flowchart of phase 4 processing. It is an example of pattern analysis. FIG. 26A is an example of an information generation system for a plurality of buildings and tenants, and FIG. 26B is an example of a data format of a log data recording unit of the information generation system of FIG. It is.

Embodiment 1 FIG.
First, Embodiment 1 of the present invention will be described in detail with reference to the drawings. The information generation system according to this embodiment is a system that generates information useful for owners and tenants of rental properties, for example.

  As shown in FIG. 1, the information generation system 100 according to this embodiment includes a data acquisition unit 1 and an analysis device 2.

The data acquisition unit 1 includes a power sensor 11, a charge data input unit 12, a CO ₂ data input unit 13, an air conditioning data input unit 14, an illumination data input unit 15, a satisfaction data input unit 16, and an environmental sensor. 17, a date and time sensor 18, a contract data input unit 19, and a rule input unit 20.

  As shown in FIG. 2, the power sensor 11 detects power data such as a power value supplied to an electrical facility installed in a room 22 as a rental office as a rental property in the building 21. The power sensor 11 is installed in a distribution board 23 that distributes and supplies power to such electrical equipment, and detects the sum of power values in the living room 22. The power data is detected by the power sensor 11 at a predetermined sampling interval and output to the analysis device 2.

  As shown in FIG. 2, the charge data input unit 12 inputs electricity charge data for use of the living room 22. The fee data input unit 12 is constructed on a computer 27 managed by the owner of the building 21. Electric power data output from the electric power sensor 11 is input to the charge data input unit 12. The charge data input unit 12 calculates the accumulated electricity charge data so far based on the power data and outputs it to the analysis device 2.

As shown in FIG. 2, the CO ₂ data input unit 13 inputs CO ₂ emission data that is emitted through the use of electrical equipment in the living room 22. The CO ₂ data input unit 13 is constructed on a computer 27 managed by the owner of the building 21. The power data output from the power sensor 11 is input to the CO ₂ data input unit 13. Based on the power data, the CO ₂ data input unit 13 calculates the accumulated CO ₂ emission data so far and outputs it to the analyzer 2.

  As shown in FIG. 2, the air-conditioning data input unit 14 inputs data (air-conditioning data) related to the operating state of the air-conditioning device 24 that is one of the electrical facilities installed in the living room 22. The air-conditioning data input unit 14 is attached to the air-conditioning device 24. For example, the air-conditioning data input unit 14 detects a power value consumed by the air-conditioning device 24 and outputs it to the analysis device 2 as air-conditioning data.

  As shown in FIG. 2, the illumination data input unit 15 inputs data (illumination data) related to the operating state of the illumination device 25 that is one of the electrical facilities installed in the living room 22. The illumination data input unit 15 is attached to the illumination device 25. For example, the illumination data input unit 15 detects a power value consumed by the illumination device 25, and outputs it to the analysis device 2 as illumination data.

  As shown in FIG. 2, the satisfaction data input unit 16 inputs tenant satisfaction data (satisfaction data) regarding the use of the living room 22. Satisfaction data is a quantification of tenant satisfaction. The satisfaction data input unit 16 is constructed on a computer 26 installed in the living room 22. The tenant inputs a satisfaction level (for example, 10-step evaluation) regarding the use of the living room 22 using the keyboard of the computer 26 or the like. The satisfaction data input unit 16 outputs satisfaction data input by the tenant to the analysis device 2.

  As shown in FIG. 2, the environmental sensor 17 detects data (environmental data) related to the environment of the living room 22. Such environmental data includes, for example, temperature data. The environmental data is detected by the environmental sensor 17 at a predetermined sampling interval, and is output to the analysis device 2.

  The date sensor 18 includes a timer (not shown). The date sensor 18 detects the year / month / date based on the count value of the timer and outputs it to the analysis device 2.

  The contract data input unit 19 inputs data related to the contract contents related to the rental property. As shown in FIG. 2, the contract data input unit 19 is constructed on a computer 27 managed by the owner. The administrator of the building 21 operates the keyboard of the computer 27 to input data related to the contract contents related to the rental property to the contract data input unit 19. By looking at this data, it is possible to grasp the current contract content of the room 22. More specifically, for example, with reference to this data, whether the room 22 is in an unused state before the contract, whether it is in the contract with the tenant, or is in use, or a predetermined timing has elapsed after the contract. Thus, for example, it is possible to determine whether it is time to send a bill (for example, an electronic bill) on a monthly basis or the timing for contract renewal.

  The rule input unit 20 inputs data related to a rule for determining an improvement measure to be presented to the tenant at the time of contract renewal of the use of the living room 22. As shown in FIG. 2, the rule input unit 20 is constructed on a computer 27 managed by the owner of the building 21. The owner operates the keyboard of the computer 27 and inputs data related to the rule to the rule input unit 20. The rule input unit 20 outputs data related to the input rule to the analysis device 2.

  The analysis device 2 includes a log data recording unit 3, a matching degree calculation unit 4, a contract content management unit 5, a specification unit 6, and a display control unit 7. The analysis device 2 is a computer. The analysis device 2 includes a CPU, a memory, and the like. The function of each component of the analysis device 2 is realized by the CPU executing the control program stored in the memory.

The log data recording unit 3 includes a power log data recording unit 31, a charge log data recording unit 32, a CO ₂ log data recording unit 33, an air conditioning log data recording unit 34, an illumination log data recording unit 35, and a satisfaction level. A log data recording unit 36, an environment log data recording unit 37, and a date / time log data recording unit 38 are provided.

  The power log data recording unit 31 records the power data output from the power sensor 11 as log data in time series.

  The charge log data recording unit 32 records the charge data output from the charge data input unit 12 as log data in time series.

The CO ₂ log data recording unit 33 records the CO ₂ emission data output from the CO ₂ data input unit 13 in time series and records it as log data.

  The air conditioning log data recording unit 34 records the air conditioning data output from the air conditioning data input unit 14 as log data in time series.

  The illumination log data recording unit 35 records the illumination data output from the illumination data input unit 15 in a time series and records it as log data.

  The satisfaction log data recording unit 36 records the satisfaction data output from the satisfaction data input unit 16 in time series and as log data.

  The environment log data recording unit 37 records the environment data output from the environment sensor 17 as log data in time series.

  The date / time log data recording unit 38 records the date / time data output from the date / time sensor 18 as log data in time series.

The match level calculation unit 4 includes a power match level calculation unit 41, a charge match level calculation unit 42, a CO ₂ match level calculation unit 43, an air conditioning match level calculation unit 44, an illumination match level calculation unit 45, and a satisfaction level. A match level calculator 46, an environment match level calculator 47, and a date / time match level calculator 48 are provided.

  As shown in FIG. 3A, the power matching degree calculation unit 41 inputs time-series data (power data A) for a recent fixed period ΔT from the power log data recording unit 31. Further, the power coincidence degree calculation unit 41 inputs from the power log data recording unit 31 time series data in the past of the power data A, that is, time series data (power data B) for a certain period ΔT with reference to the time T. To do. The power coincidence calculation unit 41 compares the power data A and the power data B. More specifically, as shown in FIG. 3B, the power matching degree calculation unit 41 superimposes the power data A on the power data B and calculates the degree of matching between them.

  Further, the power coincidence calculation unit 41 inputs the power data B based on the overlap time T while shifting the overlap time T in the time series direction, and calculates the match each time.

Charge matching degree calculation unit 42, CO ₂ matching degree calculation unit 43, air conditioning matching degree calculation unit 44, lighting matching degree calculation unit 45, satisfaction degree matching degree calculation unit 46, environment matching degree calculation unit 47, date and time matching degree calculation unit 48 As with the power coincidence calculation unit 41, the log data recording unit 3 is referred to, and the time-series data of the latest fixed period ΔT is converted into the time-series data of the fixed period ΔT based on the past time T. The degree of coincidence at the time of superposition is sequentially calculated while shifting the time of superposition.

  The contract content management unit 5 manages the contract content between the owner of the building 21 and the tenant related to the target rental property.

The specifying unit 6 includes a power matching degree calculation unit 41, a charge matching degree calculation unit 42, a CO ₂ matching degree calculation unit 43, an air conditioning matching degree calculation unit 44, an illumination matching degree calculation unit 45, a satisfaction degree matching degree calculation unit 46, an environment The degree of coincidence output from the degree of coincidence calculation unit 47 and the date / time coincidence degree calculation unit 48 is input. The specifying unit 6 specifies a time point T at which the degree of matching calculated by the degree of matching calculating unit 4 is the highest overall.

More specifically, the specifying unit 6 includes a power match degree calculation unit 41, a charge match degree calculation unit 42, a CO ₂ match degree calculation unit 43, an air conditioning match degree calculation unit 44, a lighting match degree calculation unit 45, and a satisfaction degree match. The degree of coincidence input from the degree calculating unit 46, the environment matching degree calculating unit 47, and the date / time matching degree calculating unit 48 is integrated. Subsequently, the specifying unit 6 specifies a time point T at which the integrated value is maximum.

  The display control unit 7 inputs the time T specified by the specifying unit 6. The display control unit 7 can read the log data recorded in the log data recording unit 3. Based on these inputs, the display control unit 7 controls display contents of the display device 9 to be described later.

  The analysis device 2 further includes a pattern analysis unit 8 and a rule management unit 81.

  The pattern analysis unit 8 analyzes various time series data output from the log data recording unit 3 and determines the variation pattern. The pattern analysis unit 8 stores some typical fluctuation patterns related to the data. The pattern analysis unit 8 compares the calculated variation pattern with a typical variation pattern (typical pattern), and identifies a similar typical pattern.

  The pattern analysis unit 8 stores a typical pattern in association with an improvement measure to be taken for a tenant when the typical pattern appears. The typical pattern and the improvement measure are input via the rule input unit 20.

  The pattern analysis unit 8 outputs an improvement measure corresponding to the appeared typical pattern. The display control unit 7 controls the display device 9 so that the output improvement measure is displayed.

  The display device 9 displays various time series data and improvement measures under the control of the display control unit 7. As the display device 9, as shown in FIG. 2, a computer 26 in the living room 22 can be used.

  Next, the operation of the information generation system 100 according to this embodiment will be described. FIG. 4 shows an analysis process performed by the analysis device 2. This analysis process is executed as necessary, for example, when an execution instruction is input from the owner's computer 27.

  First, the analysis apparatus 2 refers to the contract content in the contract content management unit 5 and determines whether the target rental property is before the contract (step S100). When it is before contract (step S100; Yes), the analysis apparatus 2 performs the process of phase 1 (step S101). This phase 1 processing can be said to be processing at the P (plan) stage in the PDCA cycle.

(Phase 1 processing)
First, the phase 1 process will be described. In the following, the power log data recording unit 31, the charge log data recording unit 32, the CO ₂ log data recording unit 33, the air conditioning log data recording unit 34, the lighting log data recording unit 35, the satisfaction log data recording unit 36, and the environment log data Data of a certain period ΔT including the current time output from the recording unit 37 and the date / time log data recording unit 38 are collectively expressed as current data R (m, t). Here, t is time. Moreover, m is either 1-8. R (1, t) is power data, R (2, t) is charge data, and R (3, t) is CO ₂ emission data. R (4, t) is air conditioning data, R (5, t) is illumination data, and R (6, t) is satisfaction data. R (7, t) is environment data, and R (8, t) is date / time data.

Similarly, a power log data recording unit 31, a charge log data recording unit 32, a CO ₂ log data recording unit 33, an air conditioning log data recording unit 34, an illumination log data recording unit 35, a satisfaction log data recording unit 36, and an environment log data The data of the past fixed period ΔT output from the recording unit 37 and the date / time log data recording unit 38 is hereinafter referred to as log data L (m, T). T is a reference time point (see FIG. 3B) of the power data B to be superimposed on the power data A. The log data recorded in the power log data recording unit 31 is L (1, T), the log data recorded in the charge log data recording unit 32 is L (2, T), and the CO ₂ log data The log data recorded in the recording unit 33 is L (3, T). The log data recorded in the air conditioning log data recording unit 34 is L (4, T), the log data recorded in the illumination log data recording unit 35 is L (5, T), and the satisfaction level The log data recorded in the log data recording unit 36 is L (6, T). The log data recorded in the environment log data recording unit 37 is L (7, T), and the log data recorded in the date / time log data recording unit 38 is L (8, T).

First, as shown in FIG. 5, the matching degree calculation unit 4 inputs the current data R (m, t) (step S1). That is, the power match degree calculation unit 41 inputs the current data R (1, t), the charge match degree calculation unit 42 inputs the current data R (2, t), and the CO ₂ match degree calculation unit 43 The current data R (3, t) is input. The air conditioning coincidence calculation unit 44 inputs the current data R (4, t), the illumination coincidence calculation unit 45 inputs the current data R (5, t), and the satisfaction degree coincidence calculation unit 46 Data R (6, t) is input. In addition, the environment matching degree calculation unit 47 inputs the current data R (7, t), and the date matching degree calculation unit 48 inputs the current data R (8, t).

  Subsequently, the coincidence degree calculation unit 4 initializes the count value n to 1 (step S2). The count value n is used for vector conversion of the current data R (1, t) to R (8, t).

  Subsequently, the degree-of-match calculation unit 4 decomposes the current data R (m, t) into a representative value vector Rc (m, Δt (n)) and a variation value vector Rv (m, Δt (n)). (Step S3). Here, for example, the current data R (m, t) shown in FIG. 6 is divided by the period Δt (1) as shown in FIGS. 7 and 8, and the element Rc (m, Δt ( 1), i) and the element Rv (m, Δt (1), i) of the variation vector are generated.

  More specifically, as shown in FIG. 7, the average value of the current data R (m, t) is obtained for each period Δt (1). This average value becomes each element Rc (m, Δt (1), i) of the representative value vector Rc (m, Δt (1)). Further, as shown in FIG. 8, the amount of increase / decrease in the current data R (m, t) is obtained for each period Δt (1). This increase / decrease amount becomes each element Rv (m, Δt (1), i) of the fluctuation value vector Rv (m, Δt (1)). In this way, the degree-of-match calculation unit 4 divides the current data R (m, t) into periods Δt (1), obtains an average value and an increase / decrease amount for each period, and uses them as elements. Element Rc (m, Δt (1)) of representative value vector Rc (m, Δt (1)), element Rc (m, Δt (1)) of variation value vector Rv (m, Δt (1)) , i).

Here, finally, representative value vector Rc (1, Δt (1)) of power data, fluctuation value vector Rv (1, Δt (1)), and representative value vector Rc (2, Δt (1) of charge data. )), Fluctuation value vector Rv (2, Δt (1)), representative value vector Rc (3, Δt (1)) of CO ₂ data, fluctuation value vector Rv (3, Δt (1)), and air conditioning data Representative value vector Rc (4, Δt (1)), fluctuation value vector Rv (4, Δt (1)), illumination data representative value vector Rc (5, Δt (1)), fluctuation value vector Rv (5) , Δt (1)), representative value vector Rc (6, Δt (1)) of satisfaction data, variation value vector Rv (6, Δt (1)), and representative value vector Rc (7, Δt of environmental data (1)), variation value vector Rv (7, Δt (1)), and representative value vector of date and time data Le Rc (8, Δt (1)), the variation value vector Rv and (8, Δt (1)), is obtained.

Subsequently, the coincidence degree calculation unit 4 determines whether or not the count value n exceeds N _max (step S4). N _max is the number of periods Δt (n) subject to vector decomposition. Usually, N _max >> 1, and at this time, the count value n is 1, so the determination is negative (step S4; No), and the coincidence degree calculation unit 4 increments the count value n by 1. (Step S5), the process returns to Step S3.

  Subsequently, the degree-of-match calculation unit 4 decomposes the current data R (m, t) into a representative value vector Rc (m, Δt (n)) and a variation value vector Rv (m, Δt (n)). (Step S3). Here, for example, the current data R (m, t) shown in FIG. 6 is divided by the period Δt (2) as shown in FIGS. 9 and 10, respectively, and the representative value vector Rc (m, Δt (2) ) Elements Rc (m, Δt (2), i) and each element Rv (m, Δt (2), i) of the fluctuation value vector Rv (m, Δt (2)) are generated. Δt (2) is twice as large as Δt (1).

Subsequently, the coincidence degree calculation unit 4 determines whether or not the count value n exceeds N _max (step S4). If the determination here is negative (step S4; No), the coincidence degree calculation unit 4 increments the count value n by 1 (step S5) and returns to step S3.

  Subsequently, the degree-of-match calculation unit 4 decomposes the current data R (m, t) into a representative value vector Rc (m, Δt (n)) and a variation value vector Rv (m, Δt (n)). (Step S3). Here, for example, the current data R (m, t) shown in FIG. 6 is divided by the period Δt (3) as shown in FIGS. 11 and 12, respectively, and the representative value vector Rc (m, Δt (3) ) Element Rc (m, Δt (3), i) and each element Rv (m, Δt (3), i) of the variation vector Rv (m, Δt (3)) are generated. Δt (3) is twice as large as Δt (2).

Subsequently, the coincidence degree calculation unit 4 determines whether or not the count value n exceeds N _max (step S4). If the determination here is negative (step S4; No), the coincidence degree calculation unit 4 increments the count value n by 1 (step S5) and returns to step S3.

  In this way, steps S3 → S4 → S5 are repeated, and the representative value vector Rc (m, Δt (n)) and the variation value vector Rv (m, Δt (n)) created in different periods are calculated. The

When the count value n exceeds N _max (step S4; Yes), the coincidence calculation unit 4 initializes the variable T to 0 (step S6). Subsequently, the matching degree calculation unit 4 inputs log data L (m, T) from the log data recording unit 3 (step S7). That is, the power matching degree calculation unit 41 inputs log data L (1, T), the charge matching degree calculation unit 42 inputs log data L (2, T), and the CO ₂ matching degree calculation unit 43 Log data L (3, T) is input. In addition, the air conditioning match degree calculation unit 44 inputs log data L (4, T), the illumination match degree calculation unit 45 inputs log data L (5, T), and the satisfaction degree match degree calculation unit 46 Log data L (6, T) is input. In addition, the environment matching degree calculation unit 47 inputs log data L (7, T), and the date / time matching degree calculation unit 48 inputs log data L (8, T).

  Subsequently, the coincidence degree calculation unit 4 initializes the count value n to 1 again (step S8).

  Subsequently, the degree-of-match calculation unit 4 decomposes the log data L (m, T) into a representative value vector Lc (m, Δt (n)) and a variation value vector Lv (m, Δt (n)). (Step S9). Here, for example, the current data L (m, T) shown in FIG. 13 is divided by the period Δt (1) as shown in FIGS. 14 and 15, and the representative value vector Lc (m, Δt (1)). Element Lc (m, Δt (1), i) and element Lv (m, Δt (1), i) of the variation vector Lv (m, Δt (1)) are generated.

  Subsequently, the degree-of-match calculation unit 4 uses the following equation (1) to calculate the representative value vectors Rc (m, Δt (n)) and Lc (m, Δt (n)) with the time T as a reference. The inner product Cc (m, Δt (n), T) is calculated (step S10).

ここでは、ｎ＝１であり、Ｔ＝０であるので、代表値ベクトルＲｃ（ｍ，Δｔ（１），）、Ｌｃ（ｍ，Δｔ（１））の内積Ｃｃ（ｍ，Δｔ（１），０）が算出される。

Here, since n = 1 and T = 0, the inner product Cc (m, Δt (1),) of the representative value vectors Rc (m, Δt (1)), Lc (m, Δt (1)). 0) is calculated.

  Subsequently, the degree-of-match calculation unit 4 uses the following equation (2) to calculate the variation value vectors Rv (m, Δt (n)) and Lv (m, Δt (n)) with respect to the time T. The inner product Cv (m, Δt (n), T) is calculated (step S11).

ここでは、ｎ＝１であり、Ｔ＝０であるので、変動値ベクトルＲｖ（ｍ，Δｔ（１））、Ｌｖ（ｍ，Δｔ（１））の内積Ｃｖ（ｍ，Δｔ（１），０）が算出される。

Here, since n = 1 and T = 0, the inner product Cv (m, Δt (1), 0) of the variation vector Rv (m, Δt (1)) and Lv (m, Δt (1)). ) Is calculated.

Subsequently, the coincidence degree calculation unit 4 determines whether or not the count value n exceeds N _max (step S12). Here, since the count value n is 1, the determination is negative (step S12; No), the coincidence calculation unit 4 increments the count value n by 1 (step S13), and the process returns to step S9.

  Subsequently, the degree-of-match calculation unit 4 decomposes the log data L (m, T) into a representative value vector Lc (m, Δt (n)) and a variation value vector Lv (m, Δt (n)). (Step S9). Here, for example, the current data L (m, T) shown in FIG. 13 is divided by the period Δt (2) as shown in FIGS. 16 and 17, and the representative value vector Lc (m, Δt (2)). Element Lc (m, Δt (2), i) and element Lv (m, Δt (2), i) of the fluctuation value vector Lv (m, Δt (2)) are generated.

  Subsequently, the degree-of-match calculation unit 4 uses the above formula (1) to calculate the inner product Cc () of the representative value vectors Rc (m, Δt (2)) and Lc (m, Δt (2)) at T = 0. m, Δt (2), 0) is calculated (step S10).

  Subsequently, the degree-of-match calculation unit 4 uses the above equation (2) to calculate the inner product Cv () of the variation value vectors Rv (m, Δt (2)) and Lv (m, Δt (2)) at T = 0. m, Δt (2), 0) is calculated (step S11).

Subsequently, the coincidence degree calculation unit 4 determines whether or not the count value n exceeds N _max (step S12). Here, since the count value n is 2, the determination is negative (step S12; No), and the coincidence calculation unit 4 increments the count value n by 1 (step S13), and returns to step S9.

  Subsequently, the degree-of-match calculation unit 4 decomposes the log data L (m, T) into a representative value vector Lc (m, Δt (n)) and a variation value vector Lv (m, Δt (n)). (Step S9). Here, for example, the current data L (m, T) shown in FIG. 13 is divided by the period Δt (3) as shown in FIGS. 18 and 19, and the representative value vector (m, Δt (3)) An element Lc (m, Δt (3), i) and an element Lv (m, Δt (3), i) of the variation vector Lv (m, Δt (3)) are generated.

  Subsequently, the degree-of-match calculation unit 4 uses the above equation (1) to represent the representative value vectors Rc (m, Δt (3), i), Lc (m, Δt (3), i) at T = 0. The inner product Cc (m, Δt (3), 0) is calculated (step S10).

  Subsequently, the degree-of-match calculation unit 4 uses the above equation (2) to calculate the variation vector Rv (m, Δt (3), i), Lv (m, Δt (3), i) at T = 0. The inner product Cv (m, Δt (3), 0) is calculated (step S11).

In this manner, steps S9 → S10 → S11 → S12 → S13 are repeated, and the representative value vector Lc (m, Δt (n)) and the fluctuation value vector Lv (m, Δt () of the log data L (m, T) are repeated. n)) is obtained (step S9), and the representative value vectors Rc (m, Δt (n)) and Lc (m, Δt (n)) created for the different periods Δt (1) to Δt (N _max ), respectively. Inner product Cc (m, Δt (n), T), variation value vector Rv (m, Δt (n)), and inner product Cv (m, Δt (n), T) of Lv (m, Δt (n)). Are respectively calculated (steps S10 and S11). Here, inner products Cc (m, Δt (n), 0) and Cv (m, Δt (n), T) at T = 0 are calculated.

FIG. 20 shows the inner product Cc (m, Δt (n), T) of the representative value vector and the inner product Cv of the representative value vector obtained in the period Δt (1) to Δt (N _max ). (M, Δt (n), T) is shown.

When the count value n exceeds N _max (step S12; Yes), the coincidence degree calculation unit 4 uses the following equation (3) to calculate both inner products Cc (m, Δt (n), T), Cv (m , Δt (n), T) is calculated (S14). Here, since T = 0, the integrated value S (m, 0) is obtained.

ここで、αｃ、αｖ、β（ｍ，Δｔ（ｎ））、γ（ｍ）は、係数である。αｃは、代表値ベクトルの内積Ｃｃ（ｍ，Δｔ（ｎ），Ｔ）の重みであり、αｖは、変動値ベクトルの内積Ｃｖ（ｍ，Δｔ（ｎ），Ｔ）の重みである。代表値に重点を置いて合致度を算出するのか、変動値に重点を置いて合致度を算出するのかを、αｃ、αｖの値で調整することができる。

Here, αc, αv, β (m, Δt (n)), and γ (m) are coefficients. αc is the weight of the inner product Cc (m, Δt (n), T) of the representative value vector, and αv is the weight of the inner product Cv (m, Δt (n), T) of the variation value vector. Whether the degree of coincidence is calculated with emphasis on the representative value or the degree of coincidence is calculated with emphasis on the variation value can be adjusted by the values of αc and αv.

Β (m, Δt (n)) is a weight of the period Δt (n). Of the periods Δt (1) to Δt (N _max ), what period Δt (n) is to be emphasized can be adjusted by the value of β (m, Δt (n)). it can.

γ (m) is the weight of power data, charge data, CO ₂ emission data, air conditioning data, lighting data, satisfaction data, environmental data, and date / time data. It can be adjusted by the value of γ (m) which data is to be emphasized and the degree of coincidence is calculated. In the living room 22, since the contract has not yet been made, the air conditioner 24 and the lighting device 25 as electrical facilities are not in operation. Therefore, here, the values of γ (m) other than γ (7) and γ (8) are all zero.

  The values of these coefficients can be set freely by the owner or tenant. However, the contract content management unit 5 does not use γ (m) other than γ (7) and γ (8) described above. It may be changed according to the contract contents to be managed.

  Subsequently, the specifying unit 6 adds the integrated values S (1, T) to S (8, T) to calculate the added value S (T) (step S15). In the living room 22, since the contract has not yet been made, the air conditioner 24 and the lighting device 25 as electrical facilities are not in operation. Therefore, valid current data is only environmental data and date / time data. Accordingly, here, the added value S (T) is substantially the sum of S (7, T) and S (8, T).

Subsequently, the degree-of-match calculation unit 4 determines whether or not the overlapping time T exceeds _Tmax (step S16). Here, since T = 0, the determination is negative (step S16; No), and the coincidence degree calculation unit 4 increments T by dt (step S17), and returns to step S7. Here, dt = ΔT.

Thereafter, the log data L (m, T) is input (step S7) and the count value n is initialized (step S8) while shifting the time point T by dt, and the vector decomposition of the log data L (m, T) is performed. (Step S9), calculation of inner product Cc (m, Δt (n), T) (Step S10), calculation of inner product Cv (m, Δt (n), T) (Step S11), N _max of count value n The comparison (step S12) and the increment of the count value n (step S13) are repeated. When the count value n exceeds N _max (step S12; Yes), the integrated value S (m, T) at the time T is calculated (step S14), and the added value S (T) is calculated (step S14). Step S15).

Subsequently, the degree-of-match calculation unit 4 determines whether or not the overlapping time T exceeds _Tmax (step S16). If the determination is negative (step S16; No), the coincidence degree calculation unit 4 increments T by dt (step S17) and returns to step S7.

  In this way, the accumulated value S (m, T) corresponding to each time T is calculated while shifting the time T (step S14), and the added value S (T) is calculated (step S15).

When the overlapping time T exceeds _Tmax (step S16; Yes), the specifying unit 6 specifies the time T at which the added value S (T) is maximum (step S18). The display control unit 7 reads the log data from the log data recording unit 3 on the basis of the specified time T, and displays it on the display screen of the display device 9 (step S19). The displayed data may be at least one of power data, charge data, CO ₂ emission data, air conditioning data, lighting data, satisfaction data, environmental data, and date / time data.

  The log data displayed on the display screen of the display device 9 is data related to the usage status of the rental property in the situation closest to the current situation. Therefore, the displayed data can be regarded as an estimate of how the usage state of the electrical equipment changes when it is assumed that the living room 22 is currently used. An administrator such as an owner can show a rough estimate of the usage state of the electrical equipment installed in the living room 22 by showing this data to the customer.

  After execution of step S19, the phase 1 process (step S101) is terminated. After the end of the phase 1 process, the process ends.

  The process returns to the start time of the analysis process in FIG. The analysis device 2 refers to the contract content in the contract content management unit 5 and determines whether or not the target rental property is before the contract (step S100). When the living room 22 has already been contracted (step S100; No), the analysis device 2 determines whether or not it is a predetermined timing (step S102). The predetermined timing includes, for example, the beginning of the month and a bill sending timing. When it is not a predetermined timing (step S102; No), the analysis apparatus 2 performs the process of phase 2 (step S103). This phase 2 process can be said to be a D (Do) stage process in the PDCA cycle.

(Phase 2 processing)
The phase 2 process (step S103) will be described. As shown in FIG. 21, the phase 2 process is different from the phase 1 process (see FIG. 5) in that step S30 is executed instead of step S19. In the phase 1 process, the values of γ (m) other than γ (7) and γ (8) were all 0, but in the phase 2 process, all the values of γ (m) were all free. Can be set to In FIG. 21, the processes in steps S2 to S5 are summarized as step S31.

  The display control unit 7 causes the display device 9 to compare and display the time series data of a certain period ΔT including the current time and the time series data based on the time T specified in step S18 from the log data recording unit 3. (Step S30).

  For example, it is assumed that the current time is 6 o'clock in the morning, and the fixed period ΔT is, for example, 6 hours from 0 o'clock in the evening to 6 o'clock in the morning. As shown in FIG. 22, the display control unit 7 displays the current data R (1, t) and its log data L (1, T) as data from midnight to 6:00 in the display device 9. Make a comparison display.

  Thereafter, power data is detected from the power sensor 11, and the power data is recorded in the power log data recording unit 31. The display control unit 7 inputs the latest data from the power log data recording unit 31 and displays the latest data on the display device 9 following R (1, t), and logs after the log data L (1, T). Data is also input from the power log data recording unit 31 and displayed on the display device 9.

  In the example shown in FIG. 22, the power data of today and the log data are almost the same from midnight to 6:00 am, but after 6:00 am, the power data of the day greatly increases the log data. It has exceeded. By looking at this comparison display and knowing that the current power data is higher than the comparison target, the user (tenant) using the electrical equipment, for example, during cooling, sets the set temperature higher or turns on the lighting. It becomes possible to take measures such as reducing the power supplied by darkening or the like.

  After execution of step S30, the phase 2 process (step S103) ends. After the end of the phase 2 processing, the analysis device 2 ends the processing.

  The process returns to the start time of the analysis process in FIG. The analysis device 2 refers to the contract content in the contract content management unit 5 and determines whether or not the target rental property is before the contract (step S100). When the living room 22 has already been contracted (step S100; No), the analysis device 2 determines whether or not it is a predetermined timing (step S102). If it is a predetermined timing (step S102; Yes), it is determined whether or not the contract has been updated (step S104). If the contract has not been updated (step S104; No), the process of phase 3 is executed (step S105). This phase 3 process can be said to be a C (Check) stage process in the PDCA cycle.

(Phase 3 processing)
The phase 3 process (step S105) will be described. As shown in FIG. 23, in phase 3, first, the processing of phase 2 shown in FIG. 21 is performed (step S40).

  Subsequently, the display control unit 7 causes the display device 9 to display a satisfaction level inquiry (step S41). For example, the display control unit 7 causes the display device 9 to display a message that prompts input in a 10-level evaluation of satisfaction.

  Subsequently, the satisfaction level data input unit 16 inputs the satisfaction level of the tenant (step S42). Subsequently, the satisfaction level output from the satisfaction data input unit 16 is recorded in the satisfaction log data recording unit 36 (step S43).

  After execution of step S43, the phase 3 process (step S105) is terminated. After the phase 3 processing ends, the analysis device 2 ends the processing.

  The process returns to the start time of the analysis process in FIG. The analysis device 2 refers to the contract content in the contract content management unit 5 and determines whether or not the target rental property is before the contract (step S100). When the living room 22 has already been contracted (step S100; No), the analysis device 2 determines whether or not it is a predetermined timing (step S102). If it is a predetermined timing (step S102; Yes), it is determined whether or not the contract has been updated (step S104). If the contract has been updated (step S104; Yes), the process of phase 4 is executed (step S106). This phase 4 process can be said to be an A (Act) stage process in the PDCA cycle.

(Phase 4 processing)
The phase 4 process (step S106) will be described. As shown in FIG. 24, in phase 4, first, the processing of phase 2 shown in FIG. 21 is performed (step S40).

  Subsequently, the pattern analysis unit 8 performs pattern analysis (step S50). More specifically, the pattern analysis unit 8 extracts a waveform pattern in the time-series data output from the log data recording unit 3, and an improvement measure that matches the extracted waveform pattern is managed by the rule management unit 81. Select according to the rule.

  For example, as shown in FIG. 25, let us consider a case in which the air conditioning data at the start of work (9:00) is excessive (overload) in the air conditioning data. When such a waveform pattern is obtained for air conditioning data, it is highly likely that the window has been opened for ventilation even though the air conditioner is turned on at the start of work. Therefore, when such a waveform pattern is obtained in the rule management unit 81, a rule is set in advance so that an improvement measure such as “When starting the air conditioner, be sure to close the window.” Yes. Therefore, when the pattern analysis unit 8 obtains the waveform pattern shown in FIG. 25, the above improvement measure is selected according to this rule.

  Subsequently, the display control unit 7 displays the selected improvement measure (step S51). As a result, a display such as “Be sure to close the window when turning on the air conditioner at the start of work” is displayed on the display device 9.

  After the execution of step S51, the phase 4 process (step S106) is terminated. After the end of the phase 4 process, the analysis device 2 ends the process.

  As described in detail above, according to this embodiment, the past time point T in the situation closest to the current usage state of the rental property is specified, and time-series data for a certain period with the time point T as a reference Is generated. This time series data can be regarded as estimation data of the usage situation when it is assumed that the rental property is actually used at the present time. Therefore, if the display content including this time-series data is displayed while being controlled according to the contract contents of the rental property, this time-series data can be effectively used as useful information for energy saving of the rental property. Can do.

  In addition, according to this embodiment, before the contract, the time-series data for a certain period based on the past time T in the situation closest to the current use condition of the rental property is displayed. Thereby, for example, the owner can show that the corresponding rental property realizes energy saving by showing this display to the tenant candidate.

  Further, according to this embodiment, during the contract, the current time-series data, and the time-series data for a certain period based on the past time point T in the situation closest to the current use condition of the rental property, , Are displayed in comparison. Thereby, the tenant who sees the comparison display can work for energy saving by adjusting the electrical equipment. It is one of the selling points of the rental property in the future that the tenant worked for energy saving by looking at this comparison display and realized energy saving.

  Further, according to this embodiment, when a contract is made and at a predetermined timing, the tenant's satisfaction level is input, so that time-series satisfaction data is used for the use of the electrical equipment of the rental property. Can be included as situational data. This makes it possible to evaluate rental properties based on tenant satisfaction. The high satisfaction level of tenants who have moved in the past will be one of the selling points of the rental property in the future.

  In addition, according to this embodiment, at the contract renewal timing, by analyzing the waveform pattern of the acquired time-series data, the tenant can be presented with an improvement measure regarding the usage status of the electrical equipment of the rental property. it can. Thereby, based on the past use condition, an appropriate advice can be sent to a tenant and it can be made to work for energy saving. It is one of the selling points of the rental property in the future that the tenant under contract has worked to save energy in accordance with the advice and realized energy saving.

  In addition, in this embodiment, although it was the system which produces | generates the information about the living room 22 in one building 21, the information generation system corresponding to the some building 21 or the living room 22 is also employable.

  In this case, as illustrated in FIG. 26A, the information generation system 100 is connected to a plurality of buildings 21 via a communication network 90 such as the Internet. The information generation system 100 transmits and receives necessary data to and from the building 21 via the communication network 90.

  In such a case, the log data recording unit 3 includes, as shown in FIG. 26B, the tenant specified by the building number and the building name, the tenant number, the tenant name, and the tenant business form. Each time-series data is recorded.

  As shown in FIG. 26A, the property / tenant information input unit 10 is connected to the information generation system 100. The property / tenant information input unit 10 includes information on the target building 21 and living room 22 (for example, building number or building name) and information on the target tenant (for example, tenant number, tenant name or tenant). Business form) is input and transmitted to the information generation system 100.

  In this case, the contract content management unit 5 of the information generation system 100 manages the contract content separately for rental properties or tenants. The data acquisition unit 1 communicates with the building 21 specified by the information transmitted from the property / tenant information input unit 10 and acquires various time-series data regarding the rental property and tenant of the building 21. Subsequently, the degree of coincidence calculation unit 4 calculates the degree of coincidence using time series data corresponding to the rental property or tenant specified by the information input to the property / tenant information input unit 10. Further, the display control unit 7 displays the leased property or tenant corresponding to the rental property or tenant specified by the information input to the property / tenant information input unit 10 (the corresponding rental property or tenant managed by the contract content management unit 5). The processing of phase 1 to phase 4 is selectively performed based on the contract content). Thereby, the display content of the display device 9 is controlled. In this way, as in each of the above-described embodiments, information useful for the owner of the rental property and the tenant can be generated for each rental property and for each tenant.

  In each of the above embodiments, the average value of the data within the period Δt (n) is used as the representative value. However, the present invention is not limited to this, and an intermediate value between the initial value and the final value may be used as the representative value. Further, in each of the above embodiments, the difference between the opening price and the closing price of the period Δt (n) is set as the fluctuation value. However, the present invention is not limited to this, and the difference between the opening price and the representative value, or the representative value and the closing price. The difference may be a variation value.

  In each of the above embodiments, the degree of coincidence of time-series data is calculated based on the inner product of the representative value vector and the variation vector, but the present invention is not limited to this. For example, the correlation between both time series data may be obtained based on the difference between the representative value vectors and the difference vector, and the degree of coincidence between the time series data may be calculated based on the correlation.

  In each of the above embodiments, the power sensor 11, the environment sensor 17, and the date / time sensor 18 are employed as the sensors included in the data acquisition unit 1, but other types of sensors may be employed. As the environmental sensor 17, for example, a humidity sensor may be employed.

  The programs executed by the information generation system 100 in each of the above embodiments are flexible disks, CD-ROM (Compact Disk Read-Only Memory), DVD (Digital Versatile Disk), MO (Magneto-Optical Disk), and the like. An apparatus that executes the above-described processing may be configured by storing and distributing the program in a computer-readable recording medium and installing the program.

  Further, the program may be stored in a disk device or the like of a predetermined server device on a communication network such as the Internet, and may be downloaded, for example, superimposed on a carrier wave.

  In addition, when the above functions are realized by sharing an OS (Operating System), or when the functions are realized by cooperation between the OS and an application, only the part other than the OS may be stored in a medium and distributed. You may also download it.

  The present invention can be suitably used to generate useful information for owners and tenants of rental properties such as rental offices and rental buildings.

DESCRIPTION OF SYMBOLS 1 Data acquisition part 2 Analysis apparatus 3 Log data recording part 4 Match degree calculation part 5 Contract content management part 6 Identification part 7 Display control part 8 Pattern analysis part 9 Display apparatus 10 Property / tenant information input part 11 Electric power sensor 12 Charge data input Section 13 CO ₂ data input section 14 Air conditioning data input section 15 Lighting data input section 16 Satisfaction data input section 17 Environmental sensor 18 Date / time sensor 19 Contract data input section 20 Rule input section 21 Building 22 Living room 23 Power distribution board 24 Air conditioning system 25 Lighting devices 26 and 27 Computer 31 Electric power log data recording unit 32 Charge log data recording unit 33 CO ₂ log data recording unit 34 Air conditioning log data recording unit 35 Lighting log data recording unit 36 Satisfaction log data recording unit 37 Environmental log data recording unit 38 Date and time log data recording unit 41 Power match degree calculation unit 42 Kim matching degree calculation unit 43 CO ₂ matches calculation unit 44 the air conditioning matching degree calculation unit 45 lighting match degree calculating section 46 Satisfaction matching degree calculation unit 47 environment matching degree calculation unit 48 date matches calculator 81 rule management unit 90 communication network 100 Information generation system

Claims (8)

Contract content management department that manages the contract details of rental properties,
A data acquisition unit for acquiring data on the usage status of the electrical equipment of the rental property;
A recording unit that records the data acquired by the data acquisition unit in time series and records it as time series data,
Of the time-series data recorded in the recording unit, the first time-series data of a recent fixed period is coincident with the second time-series data that is past than the first time-series data. A degree-of-match calculation unit that sequentially calculates the degree while shifting the overlapping time point;
A specifying unit for specifying a superimposition time at which the matching degree calculated by the matching degree calculating unit is the highest;
A display control unit for controlling display content including time-series data for a certain period of time based on a specified time point in the second time-series data according to the contract content managed by the contract content management unit; ,
An information generation system comprising: The display control unit
When the contract content managed by the contract content management unit indicates that the leased property is not contracted, the second time-series data has a certain period of time based on the specified time point. Control display content to display series data,
The information generation system according to claim 1. The display control unit
When the contract content managed by the contract content management unit indicates that the rental property has been contracted,
The display content is controlled so as to display a comparative display between the time series data of a certain period with the specified time point as a reference among the second time series data and the first time series data.
The information generation system according to claim 1 or 2. The data acquisition unit
Satisfaction that allows the tenant satisfaction level regarding the use of the leased property to be entered when the contract content managed by the contract content management unit indicates that the leased property has been contracted and at a predetermined timing It has a degree input part,
The recording unit is
Record the satisfaction level input to the satisfaction input unit as time series data,
The match level calculated by the match level calculation unit includes:
The degree of coincidence between the first time series data and the second time series data regarding the satisfaction level is included.
The information generation system according to claim 1, wherein the information generation system is an information generation system. A rule management unit that manages a rule between a typical waveform pattern in the first time-series data and a measure for improving the usage state of the electrical equipment;
When the contract content managed by the contract content management unit indicates that the rental property has been contracted and at a predetermined timing, the waveform pattern in the first time series data is extracted and extracted. A pattern analysis unit that selects an improvement measure corresponding to the waveform pattern according to a rule managed by the rule management unit;
Further comprising
The information generation system according to claim 1, wherein the information generation system is an information generation system. An input unit for inputting data for identifying the rental property and the tenant;
The contract content management unit
Manage the contents of the contract separately for the rental property or the tenant,
The data acquisition unit
Obtain time-series data regarding the rental property or the tenant specified by the information input in the input unit,
The recording unit is
Record time-series data for each rental property or tenant,
The degree of match calculation unit
The degree of match is calculated using time-series data corresponding to the rental property or the tenant specified by the information input to the input unit,
The display control unit
Of the contract content managed by the contract content management unit, the display content is controlled based on the contract content corresponding to the rental property or the tenant specified by the information input to the input unit,
The information generation system according to claim 1, wherein the information generation system is an information generation system. Contract content management process for managing contract details of rental properties,
A data acquisition step of acquiring data on the usage status of the electrical equipment of the rental property;
A recording step of recording the data acquired in the data acquisition step in time series and recording as time series data;
Of the time-series data recorded in the recording step, the first time-series data of a recent fixed period coincides with the second time-series data that is earlier than the first time-series data. A degree-of-match calculation step of sequentially calculating the degree while shifting the overlapping time point;
A specifying step for specifying a superimposition time at which the matching degree calculated in the matching degree calculating step is the highest;
A display control step for controlling display content including time-series data for a certain period of time based on the identified time point out of the second time-series data according to the contract content managed in the contract content management step; ,
Information generation method. Contract content management procedure for managing contract details of rental properties,
A data acquisition procedure for acquiring data relating to the usage status of the electrical equipment of the rental property;
A recording procedure for recording the data acquired in the data acquisition procedure in time series and recording as time series data,
Of the time-series data recorded by the recording procedure, the first time-series data of a recent fixed period is coincident with the second time-series data that is past than the first time-series data. A degree-of-match calculation procedure for sequentially calculating the degree while shifting the overlapping time point;
A specific procedure for specifying a superimposition time at which the degree of matching calculated in the matching level calculation procedure is the highest;
A display control procedure for controlling display content including time-series data for a certain period of time based on a specified time point in the second time-series data according to the contract content managed in the contract content management procedure; ,
A program that causes a computer to execute.

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