JP2019114169A - Health risk diagnostic method - Google Patents

Abstract

To diagnose health risk of a future resident in a life cycle of a resident in a building.SOLUTION: The present invention is directed to a method of diagnosing risk to health of a resident in a building. The health risk diagnosing method includes a step S1 of measuring a temperature in the building and an outdoor temperature in an optional date or period, a step S2 for predicting, based on the measured indoor and outdoor temperatures, a temperature in a building in a predetermined date or period, other than the optional date or period, when the risk may affect, and a step S3 for diagnosing the risk based on the predicted indoor temperature.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a method for diagnosing health risks of building occupants.

  In recent years, interest in health has been increasing, and for example, in the life cycle in a building, heat shock due to a rise in blood pressure or the like caused by a temperature difference between two rooms, and a decrease in bedroom temperature while sleeping Several health risks have been reported, such as the accompanying mid-day awakening.

  In view of such a situation, Patent Document 1 listed below measures the temperature of the living space and the non-residential space in the building with a temperature sensor, and displays the temperature difference between them to provide a life advice that promotes heat shock attention. We propose a system.

JP, 2005-249441, A

  Although the system of Patent Document 1 can display the temperature difference between a living space and a non-living space in real time, such a health risk is manifested, for example, on a specific day or period when the temperature is different from now. It can not be judged until it does.

  SUMMARY OF THE INVENTION The present invention has been made in view of the above-described actual situation, and it is a main object of the present invention to provide a method capable of diagnosing health risks of future residents in the life cycle of residents in a building. There is.

  The present invention is a method for diagnosing the risk to the health of a building occupant, comprising the steps of: measuring the temperature in the building and the temperature outside the building on any day or period; Predicting the temperature in the building of the predetermined day or period which is different from the arbitrary day or period and which affects the risk based on the temperature in the building and the temperature of the outdoor; And D. diagnosing the risk based on the temperature in the building.

  In the method of diagnosing a health risk according to the present invention, the predetermined date or period affecting the risk may include the coldest day of the year in the area where the building is present.

  In the method for diagnosing health risks according to the present invention, the risks may include heat shock due to temperature differences at different places in the building.

  In the method of diagnosing a health risk according to the present invention, the predicted temperature in the building includes the temperature of a first space in the building, and the step of diagnosing includes the temperature of the first space, and / or The heat shock may be diagnosed based on the difference between the temperature of the first space and the temperature of the second space, which is higher than the temperature of the first space.

  In the method for diagnosing health risk according to the present invention, the risk may include awakening during sleep of the resident.

  In the method for diagnosing health risks according to the present invention, the predicted temperature in the building may include the temperature of a bedroom in the building.

  The method for diagnosing health risks according to the present invention comprises the steps of measuring the temperature in the building and the temperature of the outdoors on any day or period, and based on the measured temperature in the building and the temperature of the outdoors. Predicting the temperature in the building in a predetermined day or period different from the arbitrary day or period and affecting the risk; and the risk based on the predicted temperature in the building And diagnosing. Therefore, the method of diagnosing health risks according to the present invention can diagnose, for example, whether or not the health risks become apparent on a specific day or period when the temperature is different.

It is sectional drawing which shows notionally an example of the building where the risk to a resident's health is diagnosed. It is a block diagram of a diagnostic device for enforcing the diagnostic method of a health risk. It is a flowchart which shows an example of the process sequence of the diagnostic method of a health risk. It is a flowchart which shows an example of the process sequence of a temperature prediction process. It is a graph for demonstrating the formula for estimating the temperature of 1st space. It is a graph which shows the temperature of 1st space, and the relationship of the temperature of the outdoors. It is a flowchart which shows an example of the process sequence of a 1st temperature prediction process. It is a graph for demonstrating the formula for estimating the temperature of a bedroom. It is a graph which shows the relationship between the temperature of a bedroom, and the temperature of the outdoors. It is a flowchart which shows an example of the process sequence of a 2nd temperature prediction process. It is a flowchart which shows an example of the process sequence of a diagnostic process. It is a flowchart which shows an example of the process sequence of a 1st diagnostic process. It is a flowchart which shows an example of the process sequence of a 2nd diagnostic process.

  Hereinafter, an embodiment of the present invention will be described based on the drawings. The drawings are for the purpose of enhancing the understanding of the contents of the invention, and in addition to including exaggerated displays, it is pointed out in advance that the scales do not exactly match among the drawings.

  In the method of diagnosing health risks according to the present embodiment (hereinafter sometimes referred to simply as “diagnosis method”), risks to health are diagnosed in the life cycles of the residents in the building. FIG. 1: is sectional drawing which shows notionally an example of the building 2 with which the risk to a resident's health is diagnosed.

  For example, the case where the building 2 is a house or a building is exemplified. Inside the building 2, a plurality of spaces 3 are provided. The space 3 is divided into a living room space 4 and a non-living room space 5.

  The living space 4 includes, for example, a living room 4a and a bedroom 4b. On the other hand, the non-living room space 5 includes, for example, a bathroom 5a, a dressing room (washroom) 5b, and a toilet (not shown). In the present embodiment, the air conditioner 6 capable of performing air conditioning is provided in the living room space 4, but the air conditioner 6 is not provided in the non-living room space 5. In addition, although the case where air conditioning was each carried out by the air conditioner 6 provided in each living room space 4 was illustrated in this example, you may air-condition by the whole building air conditioning system etc., for example.

  The health risks diagnosed by the diagnostic method of the present embodiment include heat shock and halfway awakening during sleep of the resident (hereinafter sometimes referred to simply as “half awakening”). The health risks to be diagnosed may be either heat shock or awakening only, or may include other health risks.

  The heat shock is caused by an increase in blood pressure or the like due to a temperature difference (for example, a temperature difference between the dressing room 5b and the living room 4a or a temperature difference between the dressing room 5b and the hot water in the bathtub) in different places in the building 2 . In order to prevent such heat shock, for example, raising the temperature of a relatively low temperature space (for example, the dressing room 5b) and / or reducing the temperature difference at different places in the building 2 Is valid.

  In the diagnosis method of the present embodiment, the heat is generated based on the difference between the temperature of the first space 11 and / or the temperature of the first space 11 and the temperature of the second space 12 whose temperature is higher than that of the first space 11. A shock is diagnosed. The first space 11 and the second space 12 can be appropriately selected as long as a temperature difference occurs. In the present embodiment, the dressing room 5 b is selected in the first space 11, and the living room 4 a is selected in the second space 12. As another example, a toilet (not shown) may be selected for the first space 11 and a living room 4 a may be selected for the second space 12.

  The halfway awakening occurs due to the temperature decrease of the bedroom 4b. In order to prevent such sudden awakening, it is effective to raise the temperature of the bedroom 4b. In the diagnosis method of the present embodiment, halfway awakening is diagnosed based on the temperature of the bedroom 4b.

  The diagnostic method of the present embodiment is implemented, for example, by a diagnostic device incorporated into one computer. The diagnosis method may be implemented, for example, by calculation by an operator. FIG. 2 is a block diagram of a diagnostic device 15 for implementing the health risk diagnostic method.

  The diagnostic device 15 of the present embodiment includes an input unit 18 as an input device, an output unit 19 as an output device, and an arithmetic processing unit 20 that decodes an instruction and performs an operation or the like.

  For example, a keyboard or a mouse is used as the input unit 18. The output unit 19 uses, for example, a display device or a printer. The arithmetic processing unit 20 includes an operation unit (CPU) 20A that performs various operations, a storage unit 20B that stores data, programs, and the like, and a work memory 20C.

  The storage unit 20B is a non-volatile information storage device made of, for example, a magnetic disk, an optical disk, or an SSD. A data unit 21 and a program unit 22 are provided in the storage unit 20B.

  The data unit 21 predicts the temperature in the building 2, a measured temperature input unit 21A for storing the temperature in the building 2 and the temperature of the outdoor 8, a living information input unit 21B for storing living information of the resident, and Calculation information input unit 21C for storing calculation formulas and constants necessary for the calculation, a predicted temperature input unit 21D for storing a predicted temperature in the building 2, and a storage result of a health risk diagnosis It comprises the diagnostic result input part 21E.

  Program unit 22 is a program executed by operation unit 20A. The program unit 22 includes a temperature prediction unit 22A for predicting the temperature in the building on a day or period that affects the health risk, and a diagnosis unit 22B for diagnosing the health risk.

  Next, the diagnosis method of the present embodiment will be described. In the diagnostic method of the present embodiment, the temperature in the building 2 of a predetermined day or period affecting the health risk (hereinafter simply referred to as “the day or period affecting the health risk” may be predicted) Health risks of residents of Building 2 are diagnosed.

  The day or period affecting the health risk can be set as appropriate. The days or periods that affect health risk preferably include, for example, the days that most affect heat shock and awakening. Thereby, health risks including heat shock and awakening can be reliably diagnosed.

  The impact on heat shock and awakening in the middle is considered to be greater on the coldest day of the year (hereinafter sometimes referred to simply as the “coldest day”) in the area where the building 2 is present. From this point of view, the day or period affecting the health risk of the present embodiment includes the coldest day of the area where the building 2 is present. In addition, about the period which influences a health risk, for example, the fluctuation | variation of the temperature in the building 2 and the temperature of the outdoors 8 is considered, for example, 3 days-about one month including the coldest day is desirable. FIG. 3 is a flowchart showing an example of the processing procedure of the health risk diagnosis method.

  In the diagnosis method of the present embodiment, the temperature in the building 2 and the temperature of the outdoor 8 are measured on any day or period (step S1). An arbitrary day or period can be set as appropriate. It is desirable to set, for example, about 3 to 10 days, in consideration of fluctuations in the temperature in the building 2 and the temperature of the outdoor 8 for an arbitrary period, for example. Further, any day or period of the present embodiment is set to a period different from the day or period affecting health risk. Thereby, the diagnostic method of the present embodiment can diagnose future or past health risks on any day or period. Note that any day or period may be the same as the day or period affecting health risk, or may include a part of the day or period affecting health risk.

  The temperature in the building 2 and the temperature of the outdoor 8 measured in the step S1 are used to predict the temperature in the building 2 on the day or period that affects the health risk in a temperature prediction step S2 described later. In addition, if there are days (due to different seasons) between any day or period and a day or period that affects health risk, the temperature in building 2 of the day or period that affects health risk can not be predicted accurately There is a fear. For this reason, it is desirable that any day or period be set between two months before the day or period affecting health risk and two months after the day or period affecting health risk.

  In step S1, as shown in FIG. 1, the temperature sensor 25 installed in the building 2 measures the temperature in the building 2 and the temperature of the outdoor 8. The temperature sensor 25 of the present embodiment is installed in advance before the diagnostic method is performed. The installation location of the temperature sensor 25 can be selected as appropriate. In the present embodiment, the temperature sensor 25 is installed in the living room 4 a, the bedroom 4 b, the dressing room 5 b, and the outdoor 8.

  The temperature sensor 25 may be connected to the diagnostic device 15 (shown in FIG. 2) or may not be connected to the diagnostic device 15. When the temperature sensor 25 is connected to the diagnostic device 15, the measurement result by the temperature sensor 25 can be sequentially transmitted to the diagnostic device 15. On the other hand, when the temperature sensor 25 is not connected to the diagnostic device, the measurement result by the temperature sensor 25 is input to the diagnostic device 15 by the operator.

  In step S1 of the present embodiment, the temperature of the living room 4a, the temperature of the bedroom 4b, the temperature of the dressing room 5b, and the temperature of the outdoor 8 are measured at predetermined time intervals on any day or period. . The time interval can be appropriately set, for example, in accordance with the prediction accuracy of the temperature in the building 2 or the like. The measurement results of the temperature in the building 2 and the temperature of the outdoor 8 are stored in the measured temperature input unit 21A (shown in FIG. 2) of the diagnostic device 15.

  Next, in the diagnosis method of the present embodiment, the temperature in the building 2 of a day or period affecting health risk is predicted based on the measured temperature in the building 2 and the temperature of the outdoor 8 (temperature prediction step) S2). In the present embodiment, the temperature in the building 2 is predicted on the day that affects the health risk (the coldest day in the present embodiment).

  The predicted temperature in the building 2 can be set as appropriate. In the present embodiment, the predicted temperature in the building 2 is the temperature of the first space 11 (in the present embodiment, the dressing room 5b) used for diagnosis of heat shock, and the bedroom 4b used for diagnosis of halfway awakening Temperature is included.

  In the temperature prediction step S2, first, the temperature in the building 2 and the temperature of the outdoor 8 stored in the measured temperature input unit 21A (shown in FIG. 2), and the temperature prediction unit 22A (shown in FIG. 2) of the program unit 22. ) Is input to the working memory 20C (shown in FIG. 2). Then, the temperature prediction unit 22A is executed by the calculation unit 20A (shown in FIG. 2). FIG. 4 is a flow chart showing an example of the processing procedure of the temperature prediction step S2.

  In the temperature prediction step S2 of the present embodiment, first, the temperature of the first space 11 (in the present embodiment, the dressing room 5b) is predicted (first temperature prediction step S21). The temperature of the first space 11 is considered to be affected by, for example, the air conditioning of the second space 12 (living room 4a), the temperature of the outdoor space 8, and the like. In the present embodiment, the temperature of the first space 11 (the dismantling house 5 b) is predicted based on the living information related to turning on / off of the air conditioning in the second space 12 and the temperature of the outdoor 8.

  The following formulas (1) and (2) are used to predict the temperature of the first space 11 (in the present embodiment, the dressing room 5b) of the present embodiment. The following formula (1) is used when the second space 12 (living room 4a) is air-conditioned. On the other hand, the following equation (2) is used when the second space 12 is not air-conditioned. FIG. 5 is a graph for explaining an equation for predicting the temperature of the first space 11. In FIG. 5, the following formula (1) is shown as a representative.

_{T 'wa = T wa -W1 ×} (T out -T' out) ... (1)
_{T 'wa = T wa -W2 ×} (T out -T' out) ... (2)
here,
_T'wa : predicted temperature of the first space _Twa : temperature of the first space _Tout : outdoor temperature _T'out : predicted outdoor temperature W1, W2: coefficient (W1 <W2)

In the above formulas (1) and (2), the temperature T _wa of the first space 11 is the first temperature measured before bathing among the temperatures of the first space 11 measured on any day or period. The temperature of the space 11 is set. In the present embodiment, for example, the average value of the temperature of the first space 11 measured from 15 to 30 minutes before the bathing start time (for example, 9 pm) to the bathing start time is the temperature _{Twa of} the first space. Set as Thereby, the temperature _Twa of the first space can be set to the temperature of the first space 11 before the room temperature rise due to the opening / closing of the door of the bathroom 5a by bathing. When the temperature of the first space 11 is continuously measured during an arbitrary period, the average value of the temperature of the first space 11 measured during the time before bathing during the arbitrary period is the first value. The temperature _{Twa of the} space 11 is set. Thereby, the influence of the temperature fluctuation of the first space 11 on the prediction accuracy can be minimized.

In the above equations (1) and (2), the temperature T _out of the outdoor 8 is set to the temperature of the outdoor 8 measured at the time before bathing among the temperatures of the outdoor 8 measured on any day or period. Be done. In the present embodiment, for example, an average value of the temperatures of the outdoor 8 measured from 15 to 30 minutes before the bathing start time to the bathing start time is set as the temperature _Tout of the outdoor 8. In addition, when the temperature of the outdoor 8 is continuously measured during an arbitrary period, the average value of the temperatures of the outdoor 8 measured at the time before bathing during the arbitrary period is the temperature T _{out of the} outdoor 8 Set to This makes it possible to minimize the influence of the temperature change of the outdoor 8 on the prediction accuracy.

In the above formulas (1) and (2), the predicted temperature _T'out of the outdoor 8 is the temperature of the outdoor 8 at the time before bathing for the day affecting the health risk. Predicted temperature T _'out of the outdoor 8 can be appropriately set. In the present embodiment, from the past weather data area building 2 is present, the predicted temperature T _'out of outdoor 8 times before bathing is obtained. In the present embodiment, for example, the average value of the temperature of the outdoor 8 predicted by bathing the start time from 15 to 30 minutes before the bath start time is set as the predicted temperature T _'out of outdoor 8.

In the above equations (1) and (2), the difference (T _out −T ′ _out ) between the measured temperature T _out of the outdoor 8 and the predicted temperature T ′ _out of the outdoor 8 is determined. The temperature difference (T _out −T ′ _out ) of the outdoor 8 is a variation of the temperature of the outdoor 8 from any day or period to a day that affects the health risk.

The coefficients W1 and W2 of the above equations (1) and (2) indicate the inclination of the graph of FIG. These coefficients W1, W2 indicates the percentage of decrease in the temperature T _wa of the first space 11 for the reduction of the temperature T _out of the outdoor 8. The temperature of the first space 11 from an arbitrary day or a period to a day that affects the health risk by multiplying the change in the temperature of the outdoor 8 (T _out −T ′ _out ) by these coefficients W1 or W2 respectively The change of (ie, _Twa - _T'wa ) is determined. Then, the temperature T _wa of the first space, the temperature decrease amount of the first space 11 (i.e., T _wa -T _'wa) by subtracting the predicted temperature T of the first space of day affect the health risk' _wa You can ask for

The coefficient W1 is set smaller than the coefficient W2. This is because the above equation (1) used when the second space 12 (in the present embodiment, the living room 4a) is air conditioned is compared to the above equation (2) used when the second space 12 is not air conditioned This is because the rate of decrease of the temperature _Twa of the first space 11 is small.

  The coefficients W1 and W2 can be set as appropriate. In the present embodiment, for example, the air conditioning (with or without) of the second space 12, the door (open or close) between the first space 11 and the second space 12, the bathing time, and the heat insulation performance of the building 2 ( The coefficients W1 and W2 are set based on the temperature of the first space 11 and the temperature of the outdoor 8 acquired under a plurality of conditions in which items including the new energy saving standard and the old energy saving standard etc. are different. Note that “the door between the first space 11 and the second space 12” is a buffer space (for example, a corridor or the like) between the first space 11 and the second space 12. It is defined as the door between the space.

  The temperature of the first space 11 and the temperature of the outdoor 8 are obtained on each day of a predetermined period under each condition. The predetermined period is set to, for example, consecutive days including both any day or period and a day (or period) affecting health risk. FIG. 6 is a graph showing the relationship between the temperature of the first space 11 and the temperature of the outdoor 8.

  In FIG. 6, one of a plurality of conditions (air conditioning in the second space 12: open, door between the first space and the second space: open, heat insulation performance of the building 2: new energy saving standard, bathing time) The temperature of the outdoor 8 acquired at 9:00 pm and the temperature of the first space 11 are shown. In FIG. 6, the temperature of the outdoor 8 and the temperature of the first space 11 are plotted for each day of a predetermined period.

  The temperature of the outdoor 8 under each condition and the temperature of the first space 11 can be acquired based on, for example, computer simulation and measurement results of the building 2. Then, under each condition, the inclination of the approximate straight line 30 based on the least square method is respectively determined for the temperature of the outdoor 8 and the temperature of the first space 11.

In the coefficient W1 of the present embodiment, among the inclinations of the approximate straight line 30 obtained under the respective conditions, the respective approximate straight lines obtained under the condition that the second space 12 (in the present embodiment, the living room 4a) is air conditioned. The central value or average value of the maximum value and the minimum value of the slope of 30 is set. This is because the air conditioning of the second space 12 has the largest influence on the temperature T _wa of the first space 11 compared to other conditions (the presence or absence of opening and closing of the door, the bathing time, and the heat insulation performance of the building 2). It is for.

  On the other hand, among the slopes of the approximate straight line 30 obtained under each condition, the coefficient W2 of the present embodiment includes the maximum value and the minimum value of the slopes of the approximate straight line 30 obtained under the condition that the second space 12 is not air conditioned. The central value or the average value of is set.

The above equations (1) and (2) in which these coefficients W1 and W2 are set are divided only by turning on and off the air conditioning of the second space 12 having the largest influence on the temperature T _wa of the first space 11. The predicted temperature _T'wa of the first space can be easily and accurately determined. Moreover, since the coefficients W1 and W2 are set by the average value of the slopes of the approximate straight lines different from the other conditions (the presence or absence of opening and closing of the door, bathing time, and the heat insulation performance of the building 2) The predicted temperature _T'wa of the first space considered can be determined.

  In the present embodiment, the coefficient W1 is set to 0.65, and the coefficient W2 is set to 0.81. The coefficients W1 and W2 are not limited to such an aspect. Such equations (1) and (2) are input to the calculation information input unit 21C before the diagnosis method is performed. FIG. 7 is a flow chart showing an example of the processing procedure of the first temperature prediction step S21.

  In the first temperature prediction step S21 of the present embodiment, first, living information of the resident is input (step S61). In step S61, the air conditioning on / off of the second space (living room 4a) shown in FIG. 1 is input by the input unit 18 (shown in FIG. 2) of the diagnostic device 15. The air conditioning on / off of the second space 12 is stored in the living information input unit 21B (shown in FIG. 2).

  Next, in the first temperature prediction step S21 of the present embodiment, it is determined whether the second space (living room 4a) is air-conditioned (step S62). In step S62, the information regarding the turning on / off of the air conditioning of the second space 12 input to the life information input unit 21B is read into the work memory 20C (shown in FIG. 2). Then, in step S62, processing is performed by execution of the temperature prediction unit 22A by the calculation unit 20A shown in FIG.

  If it is determined in step S62 that the second space (living room 4a) shown in FIG. 1 is to be air conditioned (“Y” in step S62), the health of the second space 12 in consideration of the influence of the air conditioning in the second space 12 Steps S63 and S64 of predicting the temperature of the first space 11 (the dressing room 5b) on the day that affects the risk are performed.

  On the other hand, if it is determined in step S62 that the second space (living room 4a) is not air conditioned ("N" in step S62), the influence of the air conditioning of the second space 12 is ignored, and the health risk is reduced. Step S65 which predicts the temperature of the 1st space 11 (dressing place 5b) of the day which affects is carried out.

  Next, in step S63, the temperature of the first space 11 on the day that affects the health risk is predicted in consideration of the influence of the air conditioning of the second space 12 (living room 4a). In step S63, the temperature of the first space 11 (dressing room 5b) of any day or period stored in the measured temperature input unit 21A shown in FIG. 2, the temperature of the outdoor 8, and the calculation information input unit The prediction formula (1) stored in 21C is read into the working memory 20C. Then, in step S63, processing is performed by execution of the temperature prediction unit 22A by the calculation unit 20A shown in FIG.

In step S63 in the present embodiment, first, on the basis of the above procedure (use of weather data, etc.), the predicted temperature T _'out of outdoor 8 is obtained. Then, in step S63, the equation (1), based on the temperature T _wa, the temperature T _out of the outdoor 8, and, outdoor predicted temperature T _'out of the first space 11 of any given day or time period (changing area 5b) Thus, the predicted temperature _T'wa of the first space 11 of the day that affects the health risk is determined. The predicted temperature _T'wa is stored in the predicted temperature input unit 21D (shown in FIG. 2).

By the way, even if the predicted temperature _T'wa of the first space 11 on the day that affects the health risk is high, heat shock is likely to occur if the difference between the temperature of the first space 11 and the temperature of the second space 12 is large. It is believed that. Such a temperature difference is likely to occur when the second space 12 (living room 4a) is air-conditioned. Therefore, in the next step S64, the difference between the temperature of the first space 11 (estimated temperature _T'wa ) and the temperature of the second space 12 (hereinafter simply referred to as "temperature difference") on the day affecting the health risk It is said that) T'gap is required.

In step S64, the predicted temperature T _'wa of the first space stored in the prediction temperature input unit 21D is read into the work memory 20C. Then, in step S64, processing is performed by execution of the temperature prediction unit 22A by the calculation unit 20A shown in FIG.

  As the temperature of the second space 12, the temperature actually measured in the second space 12 or the temperature of air conditioning may be set. In step S64, it is determined that the second space 12 is air-conditioned by the branch of step S62. Therefore, in the second space 12, the influence of the outside air temperature on the room temperature is small. Therefore, on the day of affecting the health risk, the temperature of the second space 12 can be considered to be equal to the temperature of the second space 12 measured on any day or period, or the temperature of the air conditioning.

In step S64 of the present embodiment, the temperature of the second space 12 is reduced by the predicted temperature _T'wa of the first space 11 of the day that affects the health risk. Thus, in step S64, the temperature of the first space 11 that affects the health risks (predicted temperature T _'wa), the temperature difference between the temperature of the second space 12 T'gap is obtained. The temperature difference T'gap is stored in the predicted temperature input unit 21D (shown in FIG. 2).

  Next, in step S65, the temperature of the first space 11 on the day that affects the health risk is predicted, ignoring the influence of air conditioning in the second space 12 (living room 4a). In step S65, the second space 12 is not air-conditioned due to the branch of step S62. For this reason, there is little possibility that the heat shock by the temperature difference of the 1st space 11 (dressing place 5b) and the 2nd space 12 may occur. For this reason, when it is determined in the branch of step S62 that the second space 12 is not air conditioned, only the temperature of the first space 11 on the day that affects the health risk is predicted. In step S65, the temperature of the first space 11 (dressing room 5b) and the temperature of the outdoors 8 of any day or period stored in the measurement temperature input unit 21A, and the temperature information of the calculation information input unit 21C The above equation (2) is read into the working memory 20C. Then, in step S65, processing is performed by execution of the temperature prediction unit 22A by the calculation unit 20A shown in FIG.

In step S65 of the present embodiment, first, the outdoor predicted temperature _T'out is acquired based on the above-described procedure (use of weather data, etc.). Then, in step S65, the equation (2), based on the temperature T _wa, the temperature T _out of the outdoor 8, and, outdoor predicted temperature T _'out of the first space 11 of any given day or time period (changing area 5b) Thus, the predicted temperature _T'wa of the first space 11 of the day that affects the health risk is determined. The predicted temperature _T'wa is stored in the predicted temperature input unit 21D (shown in FIG. 2).

  Next, as shown in FIG. 4, in the temperature prediction step S2 of the present embodiment, the temperature of the bedroom 4b (shown in FIG. 1) is predicted (second temperature prediction step S22). The temperature of the bedroom 4 b is considered to be affected by, for example, the air conditioning of the bedroom 4 b (in the present embodiment, only the beginning of sleep), the temperature of the outdoor 8, and the like. For this reason, in the present embodiment, the temperature of the bedroom 4 b is predicted based on the living information on the air conditioning of the bedroom 4 b and the temperature of the outdoor 8.

  The following formulas (3) and (4) are used to predict the temperature of the bedroom 4b of the present embodiment. The following equation (3) is used when the bedroom 4b is air conditioned. On the other hand, the following equation (4) is used when the bedroom 4b is not air conditioned. FIG. 8 is a graph for explaining an equation for predicting the temperature of the bedroom. In FIG. 8, the following equation (3) is shown as a representative.

_{T 'bed = T bed -W3 ×} (T out -T' out) ... (3)
_{T 'bed = T bed -W4 ×} (T out -T' out) ... (4)
here,
T _'bed: the predicted temperature T _bed of the bedroom: the temperature of the bedroom T _out: outdoor temperature T' _out: outdoor predicted temperature W3, W4: coefficient (W3 <W4)

In the above formulas (3) and (4), the temperature T _bed of the bedroom 4b is the lowest time of the temperatures of the bedroom 4b measured on any day or period (for example, any time between 5 am and 7 am The temperature of the bedroom 4b measured at time t) is set. In addition, when the temperature of the bedroom 4b is continuously measured during an arbitrary period, the average value of the temperature of the bedroom 4b of the above time measured during the arbitrary period is set to the temperature T _bed of the bedroom 4b. Be done. This makes it possible to minimize the influence of temperature fluctuations in the bedroom 4b on the prediction accuracy.

In the above expressions (3) and (4), the temperature T _out of the outdoor 8 is the temperature of the outdoor 8 measured at the above-mentioned time which is the lowest among the temperatures of the outdoor 8 measured on any day or period. It is set. When the temperature of the outdoor 8 is continuously measured during an arbitrary period, the average value of the temperatures of the outdoor 8 measured during the arbitrary period is set to the temperature T _out of the outdoor 8 Be done. This makes it possible to minimize the influence of the temperature change of the outdoor 8 on the prediction accuracy.

In the above formulas (3) and (4), the predicted temperature _T'out of the outdoor 8 is the temperature of the outdoor 8 at the above time of the day that affects the health risk. The predicted temperature T _'out of outdoor 8, similar to the first temperature prediction step S21, determined by historical weather data area building 2 is present.

In the above equations (3) and (4), a difference (T _out −T ′ _out ) between the measured temperature T _out of the outdoor 8 and the predicted temperature T ′ _out of the outdoor 8 is obtained. The temperature difference (T _out −T ′ _out ) of the outdoor 8 is a variation of the temperature of the outdoor 8 from any day or period to a day that affects the health risk.

The coefficients W3 and W4 of the equations (3) and (4) indicate the slope of the graph of FIG. These coefficients W3, W4 indicates the percentage of decrease in the temperature T _{bed bed} bedroom 4b for lowering of the temperature T _out of the outdoor 8. A change in temperature of the bedroom 4b from any day or period to a day that affects the health risk by multiplying the change in the temperature of the outdoor 8 (T _out -T ' _out ) by these coefficients W3 or W4 respectively The minutes (i.e., _Tbed - _T'bed ) are determined. Then, the temperature T _{bed bed} bedroom 4b, by subtracting the temperature decrement in temperature T _{bed bed} bedroom 4b, it is possible to obtain the predicted temperature T _{'bed bed} bedroom 4b day affect health risks.

  The coefficient W3 is set smaller than the coefficient W4. This is because the rate of decrease in the temperature of the bedroom 4b is smaller than that of the above-described formula (3) used when the bedroom 4b is air-conditioned, compared to the above-described formula (4) used when the bedroom 4b is not air-conditioned. is there.

  The coefficients W3 and W4 can be set as appropriate. In the present embodiment, for example, air conditioning of the bedroom 4b (no, beginning to sleep), air conditioning of the room 4c next to the bedroom (no, beginning to sleep, continuous), position of the bedroom (square room, middle room), and the building 2 The coefficients W3 and W4 are set based on the temperature of the outdoor 8 and the temperature of the bedroom 4b acquired under a plurality of conditions in which items including the heat insulation performance (new energy saving standard, old energy saving standard, etc.) are different. Ru. The beginning of air conditioning of the bedroom 4b and the next room (hereinafter, may be simply referred to as "next room") 4c indicates, for example, air conditioning of 10 pm to 1 am.

  In the item of the above conditions, the position of the bedroom (corner room, middle room) is included because the temperature of the bedroom in the corner room is likely to be lower than that of the middle room. Furthermore, in the item of the above conditions, the air conditioning of the room next to the bedroom (hereinafter sometimes referred to simply as "the next room") 4c is included because the temperature of the bedroom 4b affects the air conditioning of the next room 4c. It is to receive.

  The temperature of the outdoor 8 and the temperature of the bedroom 4b are obtained on each day of a predetermined period under each condition. The predetermined period is, for example, a continuous day including both an arbitrary day or period and a day (or period) affecting health risk. FIG. 9 is a graph showing the relationship between the temperature of the bedroom 4 b and the temperature of the outdoor 8.

  In FIG. 9, among the plurality of conditions, the bedroom acquired under one of the conditions (bedroom 4b air conditioning: beginning to sleep, next room air conditioning: no, bedroom position: corner room, insulation performance of building 2: new energy saving standard) The temperature of 4b and the temperature of outdoor 8 are shown. In FIG. 9, the temperature of the bedroom 4 b and the temperature of the outdoor 8 are plotted for each day of the predetermined period.

  The temperature of the bedroom 4b of each condition and the temperature of the outdoor 8 can be acquired based on, for example, computer simulation or a measurement result of the building 2 in practice. And in each condition, inclination of approximation straight line 30 about temperature of bedroom 4b and temperature of outdoor 8 is calculated, respectively.

In the coefficient W3 of the present embodiment, among the inclinations of the approximate straight line 30 obtained under the respective conditions, the central value of the maximum value and the minimum value of the inclinations of the approximate straight line 30 obtained under the condition that the bedroom 4b is air conditioned. Or, an average value is set. This is because the air conditioning of the bedroom 4 b has the largest influence on the temperature T _{bed of the} bedroom 4 b as compared to other conditions (air conditioning of the next room, the position of the bedroom, and the heat insulation performance of the building 2).

  On the other hand, among the inclinations of the approximate straight line 30 obtained under each condition, the center of the maximum value and the minimum value of the slopes of the approximate straight line 30 obtained under the condition that the bedroom 4b is not air conditioned. A value or an average value is set.

The above equations (3) and (4) in which these coefficients W3 and W4 are set are predicted only by turning on and off the air conditioning of the bedroom 4b having the largest influence on the temperature T _bed of the bedroom 4b, so prediction of the bedroom The temperature T ' _bed can be determined easily and accurately. Moreover, since the coefficients W3 and W4 are set by the average value of the slopes of the approximate straight lines different from the other conditions (air conditioning in the next room, the position of the bedroom, and the heat insulation performance of the building 2) The predicted bedroom temperature _T'bed can be determined.

  In the present embodiment, the coefficient W3 is set to 0.51, and the coefficient W4 is set to 0.57. The coefficients W3 and W4 are not limited to such an aspect. Such equations (3) and (4) are input to the calculation information input unit 21C before the diagnosis method is performed. FIG. 10 is a flow chart showing an example of the processing procedure of the second temperature prediction step S22.

  In the second temperature prediction step S22 of the present embodiment, first, living information of the resident is input (step S71). In step S71, the presence or absence of air conditioning (in the present embodiment, air conditioning only at the beginning of sleep) of the bedroom 4b shown in FIG. 1 is input by the input unit 18 (shown in FIG. 2) of the diagnostic device 15. The air conditioning on / off of the bedroom 4b is stored in the living information input unit 21B (shown in FIG. 2).

  Next, in the second temperature prediction step S22 of the present embodiment, it is determined whether the bedroom 4b is air conditioned (step S72). In step S72, the information on the air conditioner on / off of the bedroom 4b input to the living information input unit 21B (shown in FIG. 2) is read into the work memory 20C (shown in FIG. 2). Then, in step S72, processing is performed by execution of the temperature prediction unit 22A by the calculation unit 20A shown in FIG.

  In step S72, when it is determined that the bedroom 4b (shown in FIG. 1) is to be air-conditioned ("Y" in step S72), the bedroom on the day that affects health risk in consideration of the air conditioning of the bedroom 4b. Step S73 of predicting the temperature of 4b is performed.

  On the other hand, when it is determined in step S72 that the bedroom 4b is not air conditioned ("N" in step S72), the temperature of the bedroom 4b on the day that affects the health risk, ignoring the air conditioning of the bedroom 4b. Step S74 of predicting is performed.

  Next, in step S73, in consideration of the influence of the air conditioning of the bedroom 4b, the temperature of the bedroom 4b on a day that affects the health risk is predicted. In step S73, the temperature of the bedroom 4b of any day or period stored in the measured temperature input unit 21A (shown in FIG. 2), the temperature of the outdoor 8, and the calculation information input unit 21C (shown in FIG. 2) Is stored in the working memory 20C. Then, in step S73, processing is performed by execution of the temperature prediction unit 22A by the calculation unit 20A shown in FIG.

In step S73 of the present embodiment, first, the outdoor predicted temperature _T'out is acquired based on the above-described procedure (use of weather data, etc.). Then, in step S73, the equation (3), the temperature T _{bed bed} bedroom 4b any day or time period, the temperature T _out of the outdoor 8, and, based on the outdoor predicted temperature T _'out, affecting the health risks the predicted temperature T _'bed in the bedroom 4b of the day is required. The predicted temperature T ′ _bed is stored in the predicted temperature input unit 21D (shown in FIG. 2).

  Next, in step S74, the temperature of the bedroom 4b on the day that affects the health risk is predicted, ignoring the influence of the air conditioning in the bedroom 4b. In step S74, the temperature of the bedroom 4b of any day or period stored in the measured temperature input unit 21A (shown in FIG. 2), the temperature of the outdoor 8, and the calculation information input unit 21C (shown in FIG. 2) The above equation (4) stored in (1) is read into the working memory 20C (shown in FIG. 2). Then, in step S74, processing is performed by execution of the temperature prediction unit 22A by the calculation unit 20A shown in FIG.

In step S74 of the present embodiment, first, the outdoor predicted temperature _T'out is acquired based on the above-described procedure (use of weather data, etc.). Then, in step S74, the health risk is affected based on the above equation (4), the temperature T _bed of the bedroom 4b of any day or period, the temperature T _{out of the} outdoor 8, and the predicted outdoor temperature T ' _out. the predicted temperature T _'bed in the bedroom 4b of the day is required. The predicted temperature T ′ _bed is stored in the predicted temperature input unit 21D (shown in FIG. 2).

Next, in the diagnosis method of the present embodiment, a health risk is diagnosed based on the predicted temperature in the building 2 (diagnosis step S3). Diagnostic step S3, the predicted temperature T _'wa of the first space 11 to the day of affecting the health risks that are stored in the prediction temperature input unit 21D, the temperature difference between the temperature of the second space 12 of the first space 11 T'gap, and predicted temperature T _{'bed bed} bedroom 4b is input to the work memory 20C. Furthermore, the diagnosis unit 22B of the program unit 22 is input to the work memory 20C. Then, the diagnosis unit 22B is executed by the operation unit 20A (shown in FIG. 1). FIG. 11 is a flowchart showing an example of the processing procedure of the diagnosis step S3.

  In the diagnosis step S3 of the present embodiment, the risk of heat shock is diagnosed (first diagnosis step S31). In the first diagnosis step S31 of the present embodiment, the risk of heat shock in the first space 11 (the dressing room 5b) shown in FIG. 1 is diagnosed. FIG. 12 is a flowchart illustrating an example of the processing procedure of the first diagnosis step S31.

In the first diagnosis step S31 of the present embodiment, it is determined whether the temperature of the first space 11 (predicted temperature _T'wa ) is lower than a predetermined first threshold (step S81). The first threshold is a threshold for diagnosing the risk of heat shock in the first space 11. The first threshold can be set as appropriate. As an example, heat shock is considered to be likely to occur when the temperature of the space 3 (in the present embodiment, the first space 11) is less than 18 ° C. Therefore, 18 ° C. is set as the first threshold in the present embodiment.

  When it is determined in step S81 that the temperature of the first space 11 is lower than the first threshold (“Y” in step S81), it is diagnosed that the risk of heat shock is high (step S82). On the other hand, if it is determined in step S81 that the temperature of the first space 11 is equal to or higher than the first threshold (“N” in step S81), the next step S83 is performed.

As described above, the temperature of the first space 11 (the predicted temperature T _'wa) is even smaller than the first threshold value, a large temperature difference T'gap between the temperature of the temperature and the second space 12 of the first space 11 In some cases, it is believed that heat shock is likely to occur. Such temperature difference T'gap is likely to occur when the second space 12 (living room 4a) is air-conditioned. Therefore, in the next step S83, it is determined whether the second space 12 is to be air conditioned.

  In step S83, the information on the air conditioning on / off of the second space 12 inputted to the living information input unit 21B (shown in FIG. 2) is read into the working memory 20C (shown in FIG. 2), and the second space 12 is displayed. Is determined whether the air conditioning is to be performed. If it is determined in step S83 that the second space 12 is to be air-conditioned ("Y" in step S83), the next step S84 is performed. On the other hand, when it is determined in step S83 that the second space 12 is not air conditioned ("N" in step S83), it is diagnosed that the risk of heat shock is low (step S85).

  Next, in step S84, it is determined whether the temperature difference T'gap between the temperature of the first space 11 and the temperature of the second space 12 is larger than a predetermined second threshold. In general, heat shock is considered to be likely to occur when the temperature difference T'gap is larger than 5 ° C. Therefore, 5 ° C. is set as the second threshold in the present embodiment.

  In step S84, when the temperature difference T'gap between the temperature of the first space 11 and the temperature of the second space 12 is larger than the second threshold ("Y" in step S84), the risk of heat shock is high. It is determined (step S86). On the other hand, if the temperature difference T'gap is less than or equal to the second threshold, it is determined that the risk of heat shock is low (step S87).

Thus, in the first diagnosis step S31, the temperature of the first space 11 (predicted temperature _T'wa ) on the day that affects the health risk predicted in the temperature prediction step S2 and / or the temperature of the first space 11 And the temperature of the second space 12 (temperature difference T'gap), the risk of heat shock can be diagnosed. The diagnosis result is stored in the diagnosis result input unit 21E (shown in FIG. 2).

  Next, in the diagnosis step S3 of the present embodiment, the risk of halfway awakening is diagnosed (second diagnosis step S32). In the second diagnosis step S32 of the present embodiment, the risk of halfway awakening of the resident in the bedroom 4b is diagnosed. FIG. 13 is a flowchart illustrating an example of the processing procedure of the second diagnosis step S32.

In the second diagnosis step S32 of the present embodiment, it is determined whether the temperature of the bedroom 4b (predicted temperature T ' _bed ) is lower than a predetermined third threshold (step S91). The third threshold is a threshold for diagnosing the risk of halfway awakening in the bedroom 4b. The third threshold can be set as appropriate. In general, it is considered that halfway awakening is likely to occur when the temperature of the bedroom 4 b is less than 13 ° C. Therefore, 13 ° C. is set as the third threshold in the present embodiment.

  When it is determined in step S91 that the temperature of the bedroom 4b is lower than the third threshold ("Y" in step S91), it is diagnosed that the risk of halfway awakening is high (step S92). On the other hand, when it is determined in step S91 that the temperature of the bedroom 4b is equal to or higher than the third threshold (“N” in step S91), it is diagnosed that the risk of halfway awakening is low (step S93).

Thus, in the second diagnosis step S32, the risk of halfway awakening can be diagnosed based on the temperature (predicted temperature T ' _bed ) of the bedroom 4b on the day that affects the risk predicted in the temperature prediction step S2. . The diagnosis result is stored in the diagnosis result input unit 21E (shown in FIG. 2).

  Thus, in the diagnosis method of the present embodiment, the temperature in the building 2 on a day (or a period) affecting the risk is predicted, and the health risk (heat shock or premature awakening) is diagnosed based on the predicted temperature. be able to. Therefore, in the diagnostic method of the present embodiment, the risk can be diagnosed on any day or period.

  Furthermore, if any day or period precedes the day (or period) that affects the health risk, the health risk on a day (or period) that affects the future health risk where the temperature declines further than now It can be reliably diagnosed whether or not it becomes apparent. On the other hand, if any day or period is later than the day (or period) affecting the risk, on the day (or period) affecting the past health risk or the day (or period) affecting the future health risk It can be diagnosed whether health risks have become apparent.

Next, in the diagnosis method of the present embodiment, the health risk diagnosis result is output (step S4). In step S4 in the present embodiment, for example, the predicted temperature T of the first space 11 which is stored in the prediction temperature input section 21D (shown in FIG. 2) _'wa, and predicted temperature T bedroom 4b' _{bed bed,} and, The diagnosis result of the risk of heat shock and the risk of halfway awakening stored in the diagnosis result input unit 21E (shown in FIG. 2) is output to the display and the printer of the output unit 19, respectively. The contents to be output can be set as appropriate. Then, on the basis of the outputted diagnosis result, a proposal such as a reform for reducing the health risk and a construction such as a reform are carried out. Therefore, the diagnostic method of the present embodiment helps to reduce the health risk.

In the diagnosis step S3 of the previous embodiments, the aspect in which the health risk is diagnosed is exemplified based on the temperature in the building 2 on the day that affects the health risk, but the present invention is not limited to this aspect. In the diagnosis step S3, for example, a health risk may be diagnosed based on the temperature in the building 2 in a period affecting the risk. In this case, based on the temperature in the building 2 predicted for each day of the period affecting the health risk, the health risk may be diagnosed each day, or every day during the period affecting the risk A health risk may be diagnosed as a whole period based on the average value of the temperature in the predicted building 2. The temperature of each day building 2 belonging to period that affects the risk, the above formula (1) to (4), the predicted temperature T _'out of outdoor 8, the set temperature for each day of outdoor 8 respectively It can be easily determined by

  Thus, in the diagnostic method of this embodiment, the health risk can be diagnosed based on the temperature in the building 2 in a period that affects the risk. Thereby, in the diagnostic method of this embodiment, the health risk can be reliably diagnosed without being influenced by the temperature change in the building 2 and the temperature of the outdoor 8.

  The diagnostic device 15 of the present embodiment is exemplified as an aspect incorporated into one computer, but is not limited to such an aspect. 25 includes, for example, a client server system in which a client (terminal) having the input unit 18 and the output unit 19 shown in FIG. 2 is connected to a server having the arithmetic processing unit 20, or a client and a server via the Internet. May be configured by connected cloud computing or the like.

  As mentioned above, although the especially preferable embodiment of this invention was explained in full detail, this invention can be deform | transformed into a various aspect, and can be implemented, without being limited to embodiment of illustration.

S1 Measuring the temperature in the building and the temperature outside the building for any day or period S2 predicting the temperature in the building for the day or period affecting the risk S3 diagnosing the risk

Claims (6)

A method for diagnosing the risk to the health of the occupants of a building,
Measuring the temperature inside the building and the temperature outside on any day or period;
Based on the measured temperature in the building and the temperature of the outdoor, predict the temperature in the building for a predetermined day or period that is different from the arbitrary day or period and affects the risk The process to
Diagnosing the risk based on the predicted temperature in the building,
How to diagnose health risks.   The method for diagnosing health risk according to claim 1, wherein the predetermined day or period affecting the risk includes the coldest day of one year in the area where the building exists.   The method for diagnosing a health risk according to claim 1 or 2, wherein the risk includes heat shock caused by a temperature difference at different places in the building. The predicted temperature in the building includes the temperature of the first space in the building,
The step of diagnosing includes heat shock based on a difference between a temperature of the first space and / or a temperature of the first space and a temperature of a second space having a temperature higher than the first space. The method for diagnosing a health risk according to claim 3, wherein diagnosis is performed.   The method for diagnosing a health risk according to any one of claims 1 to 4, wherein the risk includes awakening during sleep of the resident.   The method according to claim 5, wherein the predicted temperature in the building includes the temperature of a bedroom in the building.

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