JP6769946B2 - How to diagnose health risks - Google Patents

Description

The present invention relates to a method for diagnosing a health risk of a resident of a building.

In recent years, there has been increasing interest in health. For example, in the life cycle in a building, heat shock due to an increase in blood pressure due to a temperature difference between two rooms, and a decrease in the temperature of a bedroom while sleeping, etc. Several health risks have been reported, including accompanying awakening.

In view of such a situation, the following Patent Document 1 measures the temperature between the living space and the non-living space in the building by a temperature sensor, and displays the temperature difference between them to call attention to heat shock. We are proposing a system.

Japanese Unexamined Patent Publication No. 2005-249441

In the system of Patent Document 1, the temperature difference between the living space and the non-living space can be displayed in real time, but for example, such a health risk becomes apparent on a specific day or period when the temperature is different from the present. It is not possible to judge whether or not to do so.

The present invention has been devised in view of the above circumstances, and an object of the present invention is to provide a method capable of diagnosing future resident health risks in the life cycle of resident in a building. There is.

The present invention is a method for diagnosing a health risk that occurs in a resident in a building , the method is carried out by a diagnostic device having an arithmetic processing device, and the health risk is a temperature drop in the building. Alternatively, the method comprises a heat shock caused by an increase in temperature difference at different locations in the building and an awakening during sleep of the resident caused by a temperature drop in the building. , The step of measuring the temperature inside the building and the temperature outdoors and storing them in the diagnostic device on an arbitrary day or period, and the diagnostic device measuring the temperature inside the building and the outdoor temperature. based on, different from the any day or time period, and, in the health predetermined date or period of risk is high, the step of predicting the temperature or the temperature difference within the building, said diagnostic device When the predicted temperature inside the building is lower than a predetermined threshold, or when the predicted temperature difference is larger than a predetermined threshold, the step of diagnosing that the health risk is high. It is characterized by including.

In the method for diagnosing a health risk according to the present invention, the predetermined date or period during which the health risk becomes high may include the coldest day of the year in the area where the building exists.

In the method for diagnosing a health risk according to the present invention, the predicted temperature in the building includes the temperature in the first space in the building, and in the step of diagnosing, the temperature in the first space is predetermined. When it is lower than the determined first threshold, or when the difference between the temperature of the first space and the temperature of the second space, which is higher than the first space , is larger than a predetermined second threshold. In addition, the risk of the heat shock may be diagnosed as high .

In the method for diagnosing a health risk according to the present invention, the predicted temperature inside the building may include the temperature of the bedroom of the building. Further, in the method for diagnosing a health risk according to the present invention, the step of diagnosing the whole period is based on the average value of the temperature in the building predicted for each day of the period when the health risk is high. May include the step of diagnosing the health risk of.

The method for diagnosing a health risk of the present invention is based on a step of measuring the temperature inside the building and the temperature outside the building and the measured temperature inside the building and the outside temperature on an arbitrary day or period. The risk is based on the process of predicting the temperature inside the building for a predetermined date or period that is different from the arbitrary day or period and affects the risk, and the predicted temperature inside the building. Includes the step of diagnosing. Therefore, the method for diagnosing a health risk of the present invention can, for example, diagnose whether or not the health risk becomes apparent on a specific day or period when the temperature is different from the present.

It is sectional drawing which conceptually shows an example of a building where a risk to the health of a resident is diagnosed. It is a block diagram of the diagnostic apparatus for carrying out a method of diagnosing a health risk. It is a flowchart which shows an example of the processing procedure of the health risk diagnosis method. It is a flowchart which shows an example of the processing procedure of a temperature prediction process. It is a graph for demonstrating the formula for predicting the temperature of the 1st space. It is a graph which shows the relationship between the temperature of the 1st space and the temperature of the outdoors. It is a flowchart which shows an example of the processing procedure of the 1st temperature prediction process. It is a graph for explaining the formula for predicting the temperature of a bedroom. It is a graph which shows the relationship between the temperature of the bedroom and the temperature of the outdoors. It is a flowchart which shows an example of the processing procedure of the 2nd temperature prediction process. It is a flowchart which shows an example of the processing procedure of a diagnosis process. It is a flowchart which shows an example of the processing procedure of the 1st diagnosis step. It is a flowchart which shows an example of the processing procedure of the 2nd diagnostic process.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. It should be noted that each drawing is for enhancing the understanding of the content of the invention, and in addition to including exaggerated display, it is pointed out in advance that the scales and the like do not exactly match between the drawings.

In the health risk diagnosis method of the present embodiment (hereinafter, may be simply referred to as "diagnosis method"), the health risk is diagnosed in the life cycle of the resident in the building. FIG. 1 is a cross-sectional view conceptually showing an example of a building 2 in which a risk to the health of a resident is diagnosed.

For example, the building 2 may be a house, a building, or the like. A plurality of spaces 3 are provided inside the building 2. The space 3 is divided into a living room space 4 and a non-living room space 5.

The living room 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, a toilet (not shown), and the like. In the present embodiment, the living room space 4 is provided with an air conditioner 6 capable of air conditioning, but the non-living room space 5 is not provided with an air conditioner 6. In this example, the case where the air conditioner 6 is provided in each living room space 4 is used as an example, but for example, the air conditioner may be used in the entire building.

The health risks diagnosed by the diagnostic method of the present embodiment include heat shock and mid-sleep awakening of the resident (hereinafter, may be simply referred to as "half-awakening"). The health risk to be diagnosed may be only one of heat shock and awakening, or may include other health risks.

The heat shock is caused by an increase in blood pressure due to a temperature difference in different places in the building 2 (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 order to prevent such heat shock, for example, the temperature of a space where the temperature is relatively low (for example, a dressing room 5b) is increased, and / or the temperature difference at different places in the building 2 is reduced. Is valid.

In the diagnostic method of the present embodiment, the heat is 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, which is higher than the temperature of the first space 11. Shock is diagnosed. The first space 11 and the second space 12 can be appropriately selected as long as they are places where a temperature difference occurs. In the present embodiment, the dressing room 5b is selected as the first space 11, and the living room 4a is selected as the second space 12. As another example, a toilet (not shown) may be selected for the first space 11, and a living room 4a may be selected for the second space 12.

Awakening occurs due to the temperature drop in bedroom 4b. In order to prevent such awakening, it is effective to raise the temperature of the bedroom 4b. In the diagnostic method of the present embodiment, the awakening is diagnosed based on the temperature of the bedroom 4b.

The diagnostic method of this embodiment is carried out, for example, by a diagnostic device incorporated in one computer. The diagnostic method may be carried out by, for example, calculation by an operator. FIG. 2 is a block diagram of a diagnostic device 15 for implementing a method for diagnosing a health risk.

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 the input unit 18, for example, a keyboard or a mouse is used. For the output unit 19, for example, a display device, a printer, or the like is used. The arithmetic processing device 20 includes an arithmetic unit (CPU) 20A that performs various arithmetic 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 including, for example, a magnetic disk, an optical disk, an SSD, or the like. The storage unit 20B is provided with a data unit 21 and a program unit 22.

The data unit 21 predicts the temperature inside the building 2, the measurement temperature input unit 21A for storing the temperature inside the building 2 and the temperature inside the outdoor 8, the living information input unit 21B for storing the living information of the resident, and the temperature inside the building 2. Calculation information input unit 21C for storing the calculation formulas and constants required for the above, predicted temperature input unit 21D for storing the predicted temperature inside the building 2, and for storing the diagnosis result of health risk. It is configured to include the diagnosis result input unit 21E.

The program unit 22 is a program executed by the arithmetic unit 20A. The program unit 22 includes a temperature prediction unit 22A for predicting the temperature inside the building on a day or period affecting the health risk, and a diagnosis unit 22B for diagnosing the health risk.

Next, the diagnostic method of this embodiment will be described. In the diagnostic method of the present embodiment, the temperature inside the building 2 is predicted on a predetermined date or period that affects the health risk (hereinafter, may be simply referred to as “the date or period that affects the health risk”). Therefore, the health risk of the resident of the building 2 is diagnosed.

The days or periods that affect health risk can be set as appropriate. The days or periods that affect health risk should include, for example, the days that most affect heat shock and awakening. This makes it possible to reliably diagnose health risks including heat shock and awakening.

The effects on heat shock and awakening are considered to be greater on the coldest day of the year (hereinafter, simply referred to as the "coldest day") in the area where the building 2 is located. From this point of view, the day or period that affects the health risk of the present embodiment includes the coldest day in the area where the building 2 is located. Regarding the period affecting the health risk, for example, considering the fluctuation of the temperature inside the building 2 and the temperature of the outdoor 8, it is desirable that the period is, for example, about 3 days to 1 month including the coldest day. FIG. 3 is a flowchart showing an example of a processing procedure of a method for diagnosing a health risk.

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

The temperature inside the building 2 and the temperature outside 8 measured in the step S1 are used for predicting the temperature inside the building 2 on a day or period that affects the health risk in the temperature prediction step S2 described later. If there is a vacancy (seasons are different) between an arbitrary day or period and a day or period that affects health risk, the temperature inside the building 2 cannot be accurately predicted on the day or period that affects health risk. There is a risk. For this reason, it is desirable that the arbitrary date or period be set between two months before the date or period affecting the health risk and two months after the date or period affecting the health risk.

In step S1, as shown in FIG. 1, the temperature inside the building 2 and the temperature outside 8 are measured by the temperature sensor 25 installed in the building 2. The temperature sensor 25 of the present embodiment is installed in advance before the diagnostic method is implemented. The installation location of the temperature sensor 25 can be appropriately selected. In the present embodiment, the temperature sensors 25 are installed in the living room 4a, the bedroom 4b, the dressing room 5b, and the outdoor 8.

The temperature sensor 25 may or may not be connected to the diagnostic device 15 (shown in FIG. 2). 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 operator inputs the measurement result of the temperature sensor 25 to the diagnostic device 15.

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 an arbitrary day or period. .. The time interval can be appropriately set according to, for example, the accuracy of predicting the temperature inside the building 2. The measurement results of the temperature inside the building 2 and the temperature outside 8 are stored in the measurement temperature input unit 21A (shown in FIG. 2) of the diagnostic device 15.

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

The predicted temperature inside 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 this embodiment, the dressing room 5b) used for the diagnosis of heat shock, and the bedroom 4b used for the diagnosis of awakening. Temperature is included.

In the temperature prediction step S2, first, the temperature inside the building 2 and the temperature of the outdoor 8 stored in the measurement temperature input unit 21A (shown in FIG. 2), and the temperature prediction unit 22A of the programming unit 22 (shown in FIG. 2). ) 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 flowchart showing an example of the processing procedure of the temperature prediction step S2.

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

The following equations (1) and (2) are used to predict the temperature of the first space 11 (in this 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 formula (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 equation (1) is represented 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 in the first space T _wa : Temperature in the first space T _out : 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 in the time before bathing among the temperatures of the first space 11 measured on an arbitrary day or period. The temperature of the space 11 is set. In the present embodiment, for example, the average value of the temperatures of the first space 11 measured from 15 to 30 minutes before the bathing start time (for example, 9:00 pm) to the bathing start time is the temperature T _{wa of} the first space. Is set as. As a result, the temperature T _wa of the first space can be set to the temperature of the first space 11 before the room temperature rises due to the opening and closing of the door of the bathroom 5a due to bathing. When the temperature of the first space 11 is continuously measured during an arbitrary period, the average value of the temperatures of the first space 11 measured at the time before bathing during the arbitrary period is the first. The temperature T _{wa of the} space 11 is set. As a result, the influence of the temperature fluctuation of the first space 11 on the prediction accuracy can be minimized.

In the above formulas (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 an arbitrary day or period. Will be done. In the present embodiment, for example, the 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 T _out of the outdoor 8. When the temperature of the outdoor 8 is continuously measured during an arbitrary period, the average value of the temperature of the outdoor 8 measured at the time before bathing during the arbitrary period is the temperature T _{out of the} outdoor 8. Is set to. As a result, the influence of the temperature fluctuation of the outdoor 8 on the prediction accuracy can be minimized.

In the above formulas (1) and (2), the predicted temperature _T'out of the outdoor 8 is the temperature of the outdoor 8 in the time before bathing on 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.

The formula (1) and (2), and the temperature T _out of the measured outdoor 8, 'the difference between the _out (T _out -T' predicted temperature T outdoor 8 _out) is determined. The temperature difference between the outdoor 8 (T _out -T _'out) is the change in the temperature of the outdoor 8 until the date that affect health risks from any day or period.

The coefficients W1 and W2 of the above equations (1) and (2) indicate the slope of the graph of FIG. These coefficients W1 and W2 indicate the ratio of the decrease in the temperature T _wa of the first space 11 to the decrease in the temperature T _out of the outdoor 8. These coefficients W1 or W2 is, that to be multiplied respectively in the temperature change of the outdoor _{8 (T out -T 'out)} , the temperature of the first space 11 until the day that affect health risks from any day or period (That is, T _{wa −T'wa} ) is obtained. 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 Can be sought.

The coefficient W1 is set smaller than the coefficient W2. This is because the above formula (1) used when the second space 12 (living room 4a in the present embodiment) is air-conditioned is compared with the above formula (2) used when the second space 12 is not air-conditioned. This is because the rate of decrease in the temperature T _wa 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 of the second space 12 (with or without), the door (open or closed) between the first space 11 and the second space 12, the bathing time, and the heat insulating performance of the building 2 ( The coefficients W1 and W2 are set based on the temperature of the first space 11 acquired under a plurality of conditions in which items including the new energy saving standard, the old energy saving standard, etc.) and the temperature of the outdoor 8 are obtained. The "door between the first space 11 and the second space 12" is a buffer space between the first space 11 and the second space 12 when there is a buffer space (for example, a corridor) between the first space 11 and the second space 12. Defined as a door to space.

The temperature of the first space 11 and the temperature of the outdoor 8 are acquired on each day of a predetermined period under each condition. The predetermined period is set to, for example, consecutive days including both an arbitrary day or period and a day (or period) that affects 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 the plurality of conditions (air conditioning of the second space 12: yes, 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 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 the predetermined period.

The temperature of the outdoor 8 and the temperature of the first space 11 under each condition can be acquired based on, for example, a computer simulation or a measurement result in the building 2. Then, under each condition, the slope of the approximate straight line 30 based on the least squares method is obtained for the temperature of the outdoor 8 and the temperature of the first space 11.

The coefficient W1 of the present embodiment includes each approximate straight line obtained under the condition that the second space 12 (living room 4a in the present embodiment) is air-conditioned among the slopes of the approximate straight lines 30 obtained under each condition. The center value or the average value of the maximum value and the minimum value of the inclination of 30 is set. This is because the air conditioning of the second space 12 has the greatest effect on the temperature T _wa of the first space 11 as compared with other conditions (whether the door is opened or closed, the bathing time, and the heat insulating performance of the building 2). Because.

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

Since the above equations (1) and (2) in which these coefficients W1 and W2 are set are classified only by turning on / off the air conditioner in the second space 12 which has the greatest influence on the temperature T _wa of the first space 11. , The predicted temperature _T'wa in the first space can be easily and accurately obtained. Moreover, since the coefficients W1 and W2 are set by the average value of the slopes of the approximate straight lines with different other conditions (whether the door is opened / closed, the bathing time, and the heat insulating performance of the building 2), the influence of other conditions is also affected. The predicted temperature _T'wa of the first space considered can be obtained.

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 embodiment. Such equations (1) and (2) are input to the calculation information input unit 21C before the diagnostic method is performed. FIG. 7 is a flowchart 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, the living information of the resident is input (step S61). In step S61, the on / off of the air conditioner in 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 on / off of the air conditioner in 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 or not the second space (living room 4a) is air-conditioned (step S62). In step S62, the information regarding the on / off of the air conditioner in the second space 12 input to the living information input unit 21B is read into the working memory 20C (shown in FIG. 2). Then, in step S62, processing is performed by the execution of the temperature prediction unit 22A by the calculation unit 20A shown in FIG.

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

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 effect of air conditioning in the second space 12 is ignored and it becomes a health risk. Step S65 is performed to predict the temperature of the first space 11 (dressing room 5b) on the affected day.

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 place 5b) of an arbitrary day or period stored in the measurement 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 the 21C is read into the working memory 20C. Then, in step S63, the process is performed by the 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, based on the above formula (1), the temperature T _wa of the first space 11 (dressing place 5b) for an arbitrary day or period, the temperature T _{out of the} outdoor 8, and the predicted outdoor temperature _T'out . _Therefore, the predicted temperature _T'wa of the first space 11 on the day that affects the health risk is obtained. 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 affecting the health risk is high, if the difference between the temperature of the first space 11 and the temperature of the second space 12 is large, heat shock is likely to occur. 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 (predicted temperature _T'wa ) and the temperature of the second space 12 (hereinafter, simply, "temperature difference") on the day affecting the health risk. That is the case.) 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, the process is performed by the 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 the air conditioner may be set. In step S64, it is determined that the second space 12 is air-conditioned by the branching of step S62. Therefore, in the second space 12, the influence of the outside air temperature on the indoor temperature is small. Therefore, on days affecting health risk, the temperature of the second space 12 can be considered equal to the temperature of the second space 12 measured on any day or period, or the temperature of the air conditioner.

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 on the day affecting 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. This 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 the air conditioning of the second space 12 (living room 4a). In step S65, the second space 12 is not air-conditioned due to the branching of step S62. Therefore, there is little possibility that a heat shock will occur due to the temperature difference between the first space 11 (dressing room 5b) and the second space 12. Therefore, when it is determined that the second space 12 is not air-conditioned in the branch of the step S62, 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 place 5b) of an arbitrary day or period stored in the measurement temperature input unit 21A, the temperature of the outdoor 8 and the calculation information input unit 21C are stored. The above equation (2) is read into the working memory 20C. Then, in step S65, the process is performed by the execution of the temperature prediction unit 22A by the calculation unit 20A shown in FIG.

In step S65 of the present embodiment, first, the predicted outdoor temperature _T'out is acquired based on the above-mentioned procedure (use of meteorological data, etc.). Then, in step S65, based on the above formula (2), the temperature T _wa of the first space 11 (dressing place 5b) for an arbitrary day or period, the temperature T _{out of the} outdoor 8, and the predicted outdoor temperature _T'out . _Therefore, the predicted temperature _T'wa of the first space 11 on the day that affects the health risk is obtained. 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). It is considered that the temperature of the bedroom 4b is affected by, for example, the air conditioning of the bedroom 4b (in this embodiment, only the beginning of sleep), the temperature of the outdoor 8 and the like. Therefore, in the present embodiment, the temperature of the bedroom 4b is predicted based on the living information regarding the on / off of the air conditioner of the bedroom 4b and the temperature of the outdoor 8.

The following equations (3) and (4) are used to predict the temperature of the bedroom 4b of the present embodiment. The following formula (3) is used when the bedroom 4b is air-conditioned. On the other hand, the following formula (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 represented as a representative.

_{T 'bed = T bed -W3 ×} (T out -T' out) ... (3)
_T'bed = T _bed- W4 x (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 among the temperatures of the bedroom 4b measured on any day or period (for example, any time from 5 to 7 am). The temperature of the bedroom 4b measured at) is set. When the temperature of the bedroom 4b is continuously measured during an arbitrary period, the average value of the temperature of the bedroom 4b at the above time measured during the arbitrary period is set to the temperature T _bed of the bedroom 4b. Will be done. As a result, the influence of the temperature fluctuation of the bedroom 4b on the prediction accuracy can be minimized.

In the above formulas (3) and (4), the temperature T _out of the outdoor 8 includes the temperature of the outdoor 8 measured at the time when the temperature of the outdoor 8 is the lowest among the temperatures of the outdoor 8 measured on an arbitrary day or period. Set. When the temperature of the outdoor 8 is continuously measured during an arbitrary period, the average value of the temperature of the outdoor 8 measured during the above time is set to the temperature T _out of the outdoor 8. Will be done. As a result, the influence of the temperature fluctuation of the outdoor 8 on the prediction accuracy can be minimized.

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 on the day affecting 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.

The formula (3) and (4), and the temperature T _out of the measured outdoor 8, 'the difference between the _out (T _out -T' predicted temperature T outdoor 8 _out) is determined. The temperature difference between the outdoor 8 (T _out -T _'out) is the change in the temperature of the outdoor 8 until the date that affect health risks from any day or period.

The coefficients W3 and W4 of the above equations (3) and (4) indicate the slope of the graph of FIG. These coefficients W3 and W4 indicate the ratio of the decrease in the temperature T _bed of the bedroom 4b to the decrease in the temperature T _out of the outdoor 8. These coefficients W3 or W4 is, that to be multiplied respectively outdoors 8 change in temperature of (T _out -T _'out), the change in the temperature of the bedroom 4b until the date that affect health risks from any day or period Minutes (ie, T _bed - _T'bed ) are _calculated . 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 above formula (3) used when the bedroom 4b is air-conditioned has a smaller rate of decrease in temperature of the bedroom 4b than the above 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, the air conditioning of the bedroom 4b (no, start of sleep), the air conditioning of the room 4c next to the bedroom (no, start of sleep, continuous), the position of the bedroom (corner room, middle room), and the building 2 Factors W3 and W4 are set based on the temperature of the outdoor 8 acquired under multiple conditions with different items including the heat insulation performance (new energy saving standard, old energy saving standard, etc.) and the temperature of the bedroom 4b. To. The start of sleep of the air conditioner in the bedroom 4b and the adjacent room (hereinafter, may be simply referred to as "adjacent room") 4c indicates, for example, the air conditioning from 10 pm to 1 am.

In the item of the above condition, the position of the bedroom (corner room, middle room) is included because the temperature of the bedroom of the corner room is more likely to be lower than that of the bedroom of the middle room. Further, in the item of the above conditions, the air conditioning of the room next to the bedroom (hereinafter, may be simply referred to as "adjacent room") 4c is included because the temperature of the bedroom 4b affects the air conditioning of the adjacent room 4c. To receive it.

The temperature of the outdoor 8 and the temperature of the bedroom 4b are acquired on each day of a predetermined period under each condition. The predetermined period is set to, for example, consecutive days including both an arbitrary day or period and a day (or period) that affects health risk. FIG. 9 is a graph showing the relationship between the temperature of the bedroom 4b and the temperature of the outdoor 8.

In FIG. 9, the bedroom acquired under one of the plurality of conditions (bedroom 4b air conditioning: start sleeping, next room air conditioning: none, bedroom position: corner room, building 2 insulation performance: new energy saving standard). The temperature of 4b and the temperature of outdoor 8 are shown. In FIG. 9, the temperature of the bedroom 4b and the temperature of the outdoor 8 are plotted for each day of the predetermined period.

The temperature of the bedroom 4b and the temperature of the outdoor 8 under each condition can be obtained, for example, based on a computer simulation or an actual measurement result in the building 2. Then, under each condition, the slope of the approximate straight line 30 with respect to the temperature of the bedroom 4b and the temperature of the outdoor 8 is obtained.

The coefficient W3 of the present embodiment includes the center values of the maximum and minimum slopes of the approximate straight lines 30 obtained under the condition that the bedroom 4b is air-conditioned among the slopes of the approximate straight lines 30 obtained under each condition. , Or the average value is set. This is because the air conditioning of the bedroom 4b has the greatest influence on the temperature T _{bed of the} bedroom 4b as compared with other conditions (air conditioning of the adjacent room, the position of the bedroom, and the heat insulating performance of the building 2).

On the other hand, the coefficient W4 of the present embodiment is the center of the maximum and minimum values of the slopes of the approximate straight lines 30 obtained under the condition that the bedroom 4b is not air-conditioned among the slopes of the approximate straight lines 30 obtained under each condition. A value or an average value is set.

The above equations (3) and (4) in which these coefficients W3 and W4 are set are classified only by turning on / off the air conditioning of the bedroom 4b, which has the greatest effect on the temperature T _bed of the bedroom 4b. The temperature _T'bed can be obtained easily and accurately. Moreover, since the coefficients W3 and W4 are set by the average value of the slopes of the approximate straight lines with different other conditions (air conditioning in the adjacent room, bedroom position, and heat insulation performance of the building 2), the influence of other conditions is taken into consideration. it can be determined predicted temperature T _{'bed bed} of sleeping.

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 embodiment. Such equations (3) and (4) are input to the calculation information input unit 21C before the diagnostic method is performed. FIG. 10 is a flowchart 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, the living information of the resident is input (step S71). In step S71, the presence or absence of air conditioning in the bedroom 4b shown in FIG. 1 (in this embodiment, air conditioning only at the beginning of sleep) is input by the input unit 18 (shown in FIG. 2) of the diagnostic device 15. The on / off of the air conditioner in 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 or not the bedroom 4b is air-conditioned (step S72). In step S72, the information regarding the on / off of the air conditioner of the bedroom 4b input to the living information input unit 21B (shown in FIG. 2) is read into the working memory 20C (shown in FIG. 2). Then, in step S72, processing is performed by the execution of the temperature prediction unit 22A by the calculation unit 20A shown in FIG.

When it is determined in step S72 that the bedroom 4b (shown in FIG. 1) is air-conditioned (“Y” in step S72), the bedroom on the day affecting the health risk is considered in consideration of the influence of the air conditioning of the bedroom 4b. Step S73 for predicting the temperature of 4b is carried out.

On the other hand, if it is determined in step S72 that the bedroom 4b is not air-conditioned (“N” in step S72), the effect of air conditioning on the bedroom 4b is ignored and the temperature of the bedroom 4b on the day affecting the health risk. Step S74 is carried out.

Next, in step S73, the temperature of the bedroom 4b on the day affecting the health risk is predicted in consideration of the influence of the air conditioning of the bedroom 4b. In step S73, the temperature of the bedroom 4b and the temperature of the outdoor 8 stored in the measurement temperature input unit 21A (shown in FIG. 2) for an arbitrary day or period, and the calculation information input unit 21C (shown in FIG. 2). The prediction formula (3) stored in) is read into the working memory 20C. Then, in step S73, the temperature prediction unit 22A is executed by the calculation unit 20A shown in FIG.

In step S73 of the present embodiment, first, the predicted outdoor temperature _T'out is acquired based on the above-mentioned procedure (use of meteorological data, etc.). Then, in step S73, the health risk is affected based on the above formula (3), the temperature T _bed of the bedroom 4b for an arbitrary 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 step S74, the temperature of the bedroom 4b on the day affecting the health risk is predicted, ignoring the influence of the air conditioning of the bedroom 4b. In step S74, the temperature of the bedroom 4b and the temperature of the outdoor 8 stored in the measurement temperature input unit 21A (shown in FIG. 2) for an arbitrary day or period, and the calculation information input unit 21C (shown in FIG. 2). ) Is read into the working memory 20C (shown in FIG. 2). Then, in step S74, the temperature prediction unit 22A is executed by the calculation unit 20A shown in FIG.

In step S74 of the present embodiment, first, the predicted outdoor temperature _T'out is acquired based on the above procedure (use of meteorological data, etc.). Then, in step S74, the health risk is affected based on the above formula (4), the temperature T _bed of the bedroom 4b for an arbitrary 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 diagnostic method of the present embodiment, the health risk is diagnosed based on the predicted temperature inside 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. Further, the diagnostic unit 22B of the program unit 22 is input to the working memory 20C. Then, the diagnosis unit 22B is executed by the calculation 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 diagnostic step S3 of the present embodiment, the risk of heat shock is diagnosed (first diagnostic step S31). In the first diagnostic step S31 of the present embodiment, the risk of heat shock in the first space 11 (dressing room 5b) shown in FIG. 1 is diagnosed. FIG. 12 is a flowchart showing an example of the processing procedure of the first diagnostic step S31.

In the first diagnostic step S31 of the present embodiment, it is determined whether or not the temperature (predicted temperature _T'wa ) of the first space 11 is lower than the predetermined first threshold value (step S81). The first threshold is a threshold for diagnosing the risk of heat shock in the first space 11. The first threshold value can be set as appropriate. As an example, it is considered that heat shock is likely to occur when the temperature of space 3 (first space 11 in this embodiment) is less than 18 ° C. Therefore, 18 ° C. is set as the first threshold value of this embodiment.

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

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 a 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 or not the second space 12 is air-conditioned.

In step S83, the information regarding the on / off of the air conditioner in the second space 12 input 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 determined whether or not it is air-conditioned. When it is determined in step S83 that the second space 12 is air-conditioned (“Y” in step S83), the next step S84 is carried out. On the other hand, if 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 or not 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 value. Generally, it is considered that heat shock is likely to occur when the temperature difference T'gap is larger than 5 ° C. Therefore, 5 ° C. is set as the second threshold value of this 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 value (“Y” in step S84), the risk of heat shock is high. It is determined (step S86). On the other hand, when the temperature difference T'gap is equal to or less than the second threshold value, it is determined that the risk of heat shock is low (step S87).

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

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

In the second diagnostic step S32 of the present embodiment, it is determined whether or not the temperature of the bedroom 4b (predicted temperature _T'bed ) is lower than a predetermined third threshold value (step S91). The third threshold is a threshold for diagnosing the risk of awakening in the bedroom 4b. The third threshold value can be set as appropriate. It is generally believed that mid-career awakening is more likely to occur when the temperature of bedroom 4b is less than 13 ° C. Therefore, 13 ° C. is set as the third threshold value of this embodiment.

When it is determined in step S91 that the temperature of the bedroom 4b is lower than the third threshold value (“Y” in step S91), it is diagnosed that the risk of 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 value (“N” in step S91), it is diagnosed that the risk of awakening is low (step S93).

As described above, in the second diagnostic step S32, the risk of awakening can be diagnosed based on the temperature (predicted temperature _T'bed ) of the bedroom 4b on the day affecting 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).

As described above, in the diagnostic method of the present embodiment, the temperature inside the building 2 on the day (or period) that affects the risk is predicted, and the health risk (heat shock or 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.

In addition, if any day or period is before the day (or period) that affects the health risk, then the health risk on the day (or period) that affects the future health risk, where the temperature will drop further than it is now. Can be reliably diagnosed as to whether or not is manifested. On the other hand, if any day or period is later than the date (or period) that affects the risk, on the day (or period) that affects the past health risk or the day (or period) that affects the future health risk. , It is possible to diagnose whether or not a health risk has become apparent.

Next, in the diagnostic method of the present embodiment, the diagnosis result of the health risk 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 results of the heat shock risk and the halfway awakening risk stored in the diagnosis result input unit 21E (shown in FIG. 2) are output to the display and the printer of the output unit 19, respectively. The output content can be set as appropriate. Then, based on the output diagnosis result, the proposal of the remodeling and the like for reducing the health risk and the construction of the remodeling and the like are carried out. Therefore, the diagnostic method of this embodiment is useful for reducing health risk.

In the diagnostic step S3 of the embodiments so far, an embodiment in which the health risk is diagnosed based on the temperature inside the building 2 on the day affecting the health risk has been exemplified, but the embodiment is not limited to such an embodiment. In the diagnosis step S3, the health risk may be diagnosed, for example, based on the temperature inside the building 2 during the period affecting the risk. In this case, the health risk may be diagnosed each day based on the temperature inside the building 2 predicted for each day of the period affecting the health risk, or for each day of the period affecting the risk. Health risks may be diagnosed throughout the period based on the predicted average temperature within the 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 By doing so, it can be easily obtained.

As described above, in the diagnostic method of this embodiment, the health risk can be diagnosed based on the temperature inside the building 2 during the period affecting the risk. Thereby, in the diagnostic method of this embodiment, the health risk can be reliably diagnosed without being influenced by the fluctuation of the temperature inside the building 2 and the temperature outside 8.

The diagnostic device 15 of the present embodiment has been exemplified as being incorporated in one computer, but is not limited to such a mode. Reference numeral 25 denotes, for example, a client-server system in which a client (terminal) having an input unit 18 and an output unit 19 shown in FIG. 2 and a server having an arithmetic processing device 20 are connected, or a client and a server are connected to each other via the Internet. It may be configured by cloud computing or the like connected to the client.

Although the particularly preferable embodiments of the present invention have been described in detail above, the present invention is not limited to the illustrated embodiments and can be modified into various embodiments.

S1 Step of measuring the temperature inside the building and the temperature of the outside for any day or period S2 Step of predicting the temperature inside the building for the day or period that affects the risk S3 Step of diagnosing the risk

Claims (5)

A method for diagnosing a health risk caused to residents in the building,
The method is carried out by a diagnostic device having an arithmetic processing unit.
The health risk is
Heat shock caused by a decrease in temperature inside the building or an increase in temperature difference at different locations in the building, and
Including awakening during sleep of the resident caused by the temperature drop in the building.
The method is
A step of measuring the temperature inside the building and the temperature outside the building and storing them in the diagnostic device on an arbitrary day or period.
On a predetermined date or period during which the diagnostic device differs from the arbitrary day or period and the health risk is high , based on the measured temperature inside the building and the outdoor temperature . The process of predicting the temperature inside the building or the temperature difference ,
When the diagnostic device indicates that the predicted temperature inside the building is lower than a predetermined threshold value, or when the predicted temperature difference is larger than a predetermined threshold value, the health risk is high . Including the process of diagnosing,
How to diagnose health risks. The method for diagnosing a health risk according to claim 1, wherein the predetermined date or period during which the health risk is high includes the coldest day of the year in the area where the building is located. The predicted temperature inside the building includes the temperature of the first space inside the building.
The step of diagnosing is
When the temperature of the first space is lower than the predetermined first threshold value, or
When the difference between the temperature of the first space and the temperature of the second space, which is higher than the first space, is larger than a predetermined second threshold value.
The method for diagnosing a health risk according to claim 1 or 2, wherein the risk of heat shock is diagnosed as high . The method for diagnosing a health risk according to any one of claims 1 to 3, wherein the predicted temperature inside the building includes the temperature of the bedroom of the building . The step of diagnosing the health risk includes a step of diagnosing the health risk for the entire period based on the average value of the temperature in the building predicted for each day of the period when the health risk is high. The method for diagnosing a health risk according to any one of 4 to 4.

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