JP2020115073A - Information processing device - Google Patents

Abstract

To provide an information processing device capable of generating information for maintaining an indoor environment comfortably even in a target space which an unspecified number of people enter and leave.SOLUTION: An information processing device 100 estimates a first value of an input parameter before a person enters a target space by using a human body thermal model and a target thermal distribution Img of the person entering the target space. The information processing device 100 estimates thermal sensation of a person after he/she enters the target space 2 by using the human body thermal model. The information processing device 100 accumulates the thermal sensation estimated each time the target thermal distribution Img is acquired, and generates support information 172 for supporting control of an air conditioner on the basis of people distribution 162 per thermal sensation generated from the accumulation result.SELECTED DRAWING: Figure 2

Description

The present disclosure relates to a technique for generating information that supports control of an air conditioner.

Techniques have been developed to control air conditioning using the human body heat model. For example, Japanese Unexamined Patent Application Publication No. 2018-62190 (Patent Document 1) calculates an average thermal sensation prediction from a sensible temperature calculated by inputting information such as an average amount of clothing into a human heat model, and an average thermal sensation prediction. There is disclosed an in-vehicle air conditioning system that corrects the air conditioning intensity in the vehicle based on the above.

JP, 2008-62190, A

J. A. J. Stolwijk and J. D. Hardy, ``Temperature Regulation in Man-A Theoretical Study'' Pfluegers Archiv 291, pp129-162, 1966

However, in the technique described in Patent Document 1, the average amount of clothing input to the human body heat model is determined based on the information of the average age of the user depending on the line segment or the time zone. Therefore, the environment of the target space cannot be made comfortable according to the states of an unspecified number of persons who are actually in the vehicle. Therefore, there is a demand for a technique capable of generating information for maintaining the environment in the target space comfortably according to the state of the person even in the target space where an unspecified number of people come and go. There is.

According to one aspect, the information processing device includes an acquisition unit for acquiring a target heat distribution generated by the heat distribution measuring device measuring a person who enters a target space, and an input parameter that affects the heat of the human body. A first estimation for estimating the first value of the input parameter before the person enters the target space by using the human heat model that receives the value and outputs the value of the output parameter related to the heat of the human body, and the target heat distribution Part, and a second estimation part for estimating the thermal sensation of the person after entering the target space using the human body heat model, and the second estimation part each time the acquisition part acquires the target heat distribution. A distribution generation unit for accumulating the sensed thermal sensation in a predetermined period and generating a population distribution for each thermal sensation from the accumulation result, and support information for supporting control of the air conditioner based on the population distribution. And an information generating unit for generating. The second estimation unit uses the first value of the input parameter estimated by the first estimation unit as the initial value of the output parameter obtained by inputting the first value of the input parameter to the human body heat model, and then inputs the same after entering the target space. The second value of the output parameter is calculated by inputting the second value of the parameter to the human body heat model. The second estimation unit estimates the thermal sensation of the person based on the second value of the output parameter.

Preferably, the input parameters include the amount of clothes, the amount of metabolism, the weight, and the body surface area of the person. The output parameters include surface temperatures of multiple parts of the person, including clothing. The first estimation unit estimates the weight and body surface area of the person based on the size of the region corresponding to the person in the target heat distribution. The first estimation unit estimates the value of the clothing amount of the person based on the temperature distribution of the area in the target heat distribution. The first estimating unit is configured to input the estimated values of the body weight, the body surface area, and the amount of clothing, and the values of the metabolic amount corresponding to each of the plurality of virtual patterns into the human body heat model. The surface temperature is compared with the temperature distribution of the area. The first estimation unit extracts, from the plurality of virtual patterns, a virtual pattern corresponding to the surface temperatures of the plurality of portions that are closest to the temperature distribution of the area. The first estimation unit estimates the value of the metabolic rate corresponding to the extracted virtual pattern as the value of the metabolic rate of the person before entering the target space.

Preferably, the input parameters include the amount of clothes, the amount of metabolism, the weight, and the body surface area of the person. The output parameters include surface temperatures of multiple parts of the person, including clothing. The first estimation unit estimates the weight and body surface area of the person based on the size of the region corresponding to the person in the target heat distribution. The first estimation unit obtains the estimated values of the body weight and the body surface area, and the values of the clothing amount and the metabolism amount corresponding to each of the plurality of virtual patterns, which are obtained by an input process of inputting the human body heat model. Compare the surface temperature of the area and the temperature distribution of the area. The first estimation unit extracts, from the plurality of virtual patterns, a virtual pattern corresponding to the surface temperatures of the plurality of portions that are closest to the temperature distribution of the area. The first estimating unit estimates a provisional value of the amount of clothing corresponding to the extracted virtual pattern as a value of the amount of clothing of a person. The first estimation unit estimates the value of the metabolic rate corresponding to the extracted virtual pattern as the value of the metabolic rate of the person before entering the target space.

Preferably, the input parameters further include temperature, humidity, wind speed and radiant heat around the person. In the input processing, the first estimation unit further uses, as a human body heat model, the values of humidity, wind speed, and radiant heat quantity determined according to the environment outside the target space, and the temperature values corresponding to each of the plurality of virtual patterns. input. The first estimation unit estimates a temperature value corresponding to the extracted virtual pattern as a temperature value around the person before entering the target space.

Preferably, the second value of the input parameter is the value of the weight, body surface area, and clothing amount of the person estimated by the first estimation unit, and the value of the metabolic amount determined from the physical activity assumed in the target space. Including.

Preferably, the second value of the input parameter is the value of the weight, body surface area, and clothing amount of the person estimated by the first estimation unit, and the value of the metabolic amount determined from the physical activity assumed in the target space, It includes values of temperature, humidity, wind speed, and radiant heat that are determined according to the environment in the target space.

Preferably, the acquisition unit receives the output value of the distance measuring sensor for measuring the distance to the person and targets the heat distribution measured by the heat distribution measuring device when the output value reaches a predetermined distance. Obtain as heat distribution.

Preferably, the information processing device further includes an air conditioning control unit for controlling the air conditioning device based on the information.

According to another aspect, the information processing method includes a step of acquiring a target heat distribution generated by a heat distribution measuring device measuring a person who enters a target space, and a value of an input parameter that affects heat of a human body. A step of estimating a first value of the input parameter before the person enters the target space by using the human body heat model that receives and outputs the value of the output parameter related to the heat of the human body, and the human body heat model Using the step of estimating the thermal sensation of the person after entering the target space, the thermal sensation estimated every time the target heat distribution is acquired is accumulated for a predetermined period, and the thermal sensation is calculated from the accumulation result. And a step of generating support information for supporting control of the air conditioner based on the distribution of the number of people. The step of estimating the thermal sensation of a person is performed by using the first value of the output parameter obtained by inputting the first value of the input parameter to the human body heat model as an initial value, and the first value of the input parameter after entering the target space. The method includes the steps of calculating the second value of the output parameter by inputting the two values into the human body heat model, and estimating the thermal sensation of the person based on the second value of the output parameter.

In another aspect, the program causes a computer to execute each step of the above information processing method.

In a certain aspect, even in a target space where an unspecified number of persons enter and leave, it is possible to generate information for maintaining the environment in the target space comfortably according to the state of the person.

It is a figure which shows the mode of the room in which the air-conditioning system according to embodiment is installed. FIG. 3 is a diagram showing an example of a functional configuration of an information processing device according to the first embodiment. It is a figure explaining the determination method of the presence or absence of the person's entering a room by a heat distribution acquisition part. It is a figure explaining the method of the cutting-out process by a heat distribution process part. It is a figure explaining the method of the division process by a heat distribution process part. It is a figure which shows an example of the temperature change by clothing typically. It is a figure which shows an example of the correlation with the amount of clothes and the difference of the surface temperature of the outermost clothes, and skin temperature. It is a figure explaining the estimation method of height by a human body size estimation part. It is a figure which shows an example of the correlation of height and standard weight. It is a figure which shows an example of a human body heat model. It is a figure which shows an example of each temporary skin temperature of a some virtual pattern. It is a figure which shows an example of a change of a person's thermal sensation after entering a target space. It is a figure which shows the specific example of the number distribution of people produced|generated by the distribution production|generation part. It is a flow chart which shows a flow of processing which generates history data. It is a flowchart which shows the flow of the process which controls an air conditioner using the produced|generated historical data. It is a block diagram which shows the main hardware constitutions of an information processing apparatus. FIG. 7 is a diagram showing an example of a functional configuration of an information processing device according to a second embodiment. It is a figure showing seven clothing patterns assumed in the summer. It is a figure which shows eight dressing patterns assumed in an intermediate period (spring and autumn). It is a figure which shows eight dressing patterns assumed in winter.

Hereinafter, an embodiment according to the present invention will be described with reference to the drawings. In the following description, the same parts and components are designated by the same reference numerals. Their names and functions are also the same. Therefore, detailed description thereof will not be repeated. Note that the embodiments and the modifications described below may be selectively combined as appropriate.

<A. Embodiment 1>
[A1. Air conditioning system]
An air conditioning system 1 according to the first embodiment will be described with reference to FIG. FIG. 1 is a diagram showing a state in a room where an air conditioning system 1 according to the first embodiment is installed.

The air conditioning system 1 is installed, for example, in a target space 2 where an unspecified number of people enter and leave. The target space 2 is, for example, a store such as a supermarket, a condominium, a hospital, a school, or another space where an unspecified number of people come and go.

The air conditioning system 1 includes a distance measuring sensor 50, a heat distribution measuring device 51, temperature/humidity sensors 52, 53A, 53B, an operation panel 54, an information processing device 100, and an air conditioning device 200.

The distance measuring sensor 50 is installed near the entrance/exit 4 of the target space 2 and measures the distance to a person passing through the entrance/exit 4. As an example, the distance measuring sensor 50 may be an infrared sensor, an ultrasonic sensor, or the like.

The heat distribution measuring device 51 is installed in the vicinity of the entrance/exit 4 of the target space 2 and measures the heat distribution indicating the distribution of the surface temperature of an object (including a person) near the entrance/exit 4. As an example, the heat distribution measuring device 51 employs infrared thermography.

The distance measuring sensor 50 and the heat distribution measuring device 51 are communicatively connected to the information processing device 100, and the output value from the distance measuring sensor 50 and the heat distribution from the heat distribution measuring device 51 are transmitted to the information processing device 100 in a predetermined cycle. Is output with. Communication between the distance measuring sensor 50, the heat distribution measuring device 51, and the information processing device 100 is realized by wireless communication or wired communication.

The air conditioner 200 includes an indoor unit 200A, a ventilation device 200B, an outdoor unit 200C, and a blower 200D.

The temperature/humidity sensor 52 is installed, for example, in the target space 2 and detects the temperature and humidity in the room. The temperature/humidity sensors 53A and 53B are installed, for example, outside the target space 2 and detect the temperature and humidity outside the room. Hereinafter, unless the temperature/humidity sensors 53A and 53B are particularly distinguished, each of the temperature/humidity sensors 53A and 53B is referred to as a “temperature/humidity sensor 53”. The temperature/humidity sensors 52 and 53A are installed, for example, near the entrance/exit 4 of the target space 2. The temperature/humidity sensor 53B is installed, for example, near the outdoor unit 200C. The temperature/humidity sensors 52 and 53 are communicatively connected to the information processing apparatus 100, and the output values from the temperature/humidity sensors 52 and 53 are output to the information processing apparatus 100 in a predetermined cycle. Communication between the temperature and humidity sensors 52 and 53 and the information processing device 100 is realized by wireless communication or wire communication.

The operation panel 54 is an input device for receiving an operation input to the air conditioner 200. As an example, the operation panel 54 receives power ON/OFF operation, room temperature setting operation, operation mode (for example, cooling mode or heating mode) switching operation, and the like.

The information processing device 100 is, for example, a PC (Personal Computer) or a server. The information processing device 100, based on the output value of the distance measuring sensor 50, the heat distribution of the heat distribution measuring device 51, and the output values of the temperature and humidity sensors 52 and 53, information for supporting the control of the air conditioning device 200 (hereinafter, "Support information") is generated. Details of information processing by the information processing apparatus 100 will be described later. The information processing device 100 is communicatively connected to the air conditioning device 200. Communication between the information processing device 100 and the air conditioning device 200 is realized by wireless communication or wired communication.

The indoor unit 200A is a machine for adjusting the temperature and humidity of the target space 2. The ventilation device 200B is a machine for replacing the air inside and outside the target space. The outdoor unit 200C is connected to the indoor unit 200A via a pipe. A refrigerant as a heat exchange medium flows in the pipe, and heat exchange between the inside and the outside of the target space is performed via the refrigerant. The blower 200D is a machine for generating convection of air in the target space 2, and is configured by, for example, a ceiling fan, a circulator, or the like.

Although FIG. 1 shows an example in which the air conditioning system 1 is composed of temperature and humidity sensors 52 and 53, the air conditioning system 1 has at least one temperature and humidity for detecting the temperature and humidity in the target space 2. It may be configured by a sensor and at least one temperature/humidity sensor that detects the temperature/humidity outside the target space 2.

1 shows an example in which the air conditioner 200 includes one indoor unit 200A, an outdoor unit 200C, two ventilation devices 200B, and one blower 200D, the air conditioner 200 includes an indoor unit and an outdoor unit. And either a ventilator or a blower. For example, the air conditioner 200 may include only the indoor unit and the outdoor unit, and may not include the ventilation device and the blower.

Further, although FIG. 1 shows an example in which the air conditioner 200 includes one indoor unit 200A, the air conditioner 200 may include two or more indoor units. Similarly, although FIG. 1 shows an example in which the air conditioner 200 includes two ventilation devices 200B, the air conditioner 200 may include one or more than three ventilation devices. Similarly, although FIG. 1 shows an example in which the air conditioner 200 includes one outdoor unit 200C, the air conditioner 200 may include two or more outdoor units. Similarly, although FIG. 1 shows an example in which the air conditioner 200 includes one blower 200D, the air conditioner 200 may include two or more blowers. For example, the air conditioner 200 may include a ceiling fan and a circulator.

[A2. Functional configuration of information processing device]
The functional configuration of the information processing apparatus 100 will be described with reference to FIGS. FIG. 2 is a diagram showing an example of a functional configuration of the information processing apparatus according to the first embodiment.

As shown in FIG. 2, the information processing apparatus 100 has a heat distribution acquisition unit 110, a heat distribution processing unit 112, a surface temperature measurement unit 114, a first estimation unit 120, and a second estimation unit as main functional configurations. The estimation unit 140, the storage device 150, the distribution generation unit 160, the support information generation unit 170, and the air conditioning control unit 180 are included. Hereinafter, these functional configurations will be described in order.

[A2-1. Heat distribution acquisition unit 110]
The heat distribution acquisition unit 110 acquires a target heat distribution Img generated by the heat distribution measuring device 51 measuring a person who enters the target space 2. Specifically, the heat distribution acquisition unit 110 determines whether or not a person has entered the target space 2 through the doorway 4 based on the output value from the distance measuring sensor 50. The heat distribution acquisition unit 110 acquires, as the target heat distribution Img, the heat distribution measured by the heat distribution measuring device 51 at the timing when it is determined that a person has entered the target space 2.

A method of acquiring the target heat distribution Img by the heat distribution acquisition unit 110 will be described with reference to FIG. 3. FIG. 3 is a diagram illustrating a method of determining whether or not a person has entered the room by the heat distribution acquisition unit 110. The heat distribution acquisition unit 110 constantly monitors the output value from the distance measuring sensor 50. When a person passes through the entrance/exit 4, the output value from the distance measuring sensor 50 changes. When the distance measuring sensor 50 is installed toward the doorway 4 in the target space 2 and a person is moving through the doorway 4 in the direction from the outside to the inside, the output value from the distance measuring sensor 50 is It becomes smaller as the person moves.

Specifically, as shown in FIG. 3A, when a person does not pass through the entrance/exit 4 (3 seconds before the completion of entering the person), the output value of the distance measuring sensor 50 does not change. .. As shown in FIG. 3B, when a person starts entering the room through the entrance/exit 4 (2 seconds before the completion of entering the person), the output value of the distance measuring sensor 50 is, for example, the distance to the person. It shows that it is 5 m. As shown in FIG. 3C, when the person completes entering the room through the entrance/exit 4, the output value of the distance measuring sensor 50 indicates that the distance to the person is 3 m, for example.

The heat distribution acquisition unit 110 changes the output value from the distance measuring sensor 50, and at a timing when the output value reaches a predetermined distance from a value larger than a predetermined distance (for example, 3 m), It is determined that a person has entered the room.

The heat distribution acquisition unit 110 accumulates the heat distribution received from the heat distribution measuring device 51 for a predetermined period (for example, 5 seconds). The heat distribution processing unit 112 acquires, as the target heat distribution Img, the heat distribution measured by the heat distribution measuring device 51 from the accumulated heat distributions at the first timing when it is determined that the person has entered the room. Further, the heat distribution acquisition unit 110 obtains the heat distribution measured by the heat distribution measuring device 51 at the second timing (for example, the timing 3 seconds before the first timing) at which the output value from the distance measuring sensor 50 does not change. The background heat distribution Img0 is acquired. The heat distribution acquisition unit 110 outputs the acquired target heat distribution Img and background heat distribution Img0 to the heat distribution processing unit 112.

[A2-2. Heat distribution processing unit 112]
The heat distribution processing unit 112 performs a cutting process of cutting out a region corresponding to a person from the target heat distribution Img and a dividing process of dividing the cut region into a plurality of parts.

The processing method of the heat distribution processing unit 112 will be described with reference to FIGS. 4 and 5. FIG. 4 is a diagram illustrating a method of cutout processing by the heat distribution processing unit 112. As illustrated in FIG. 4, the heat distribution processing unit 112 calculates a difference between the target heat distribution Img and the background heat distribution Img0, and cuts out a region including pixels in which the difference exceeds a threshold value as a region R corresponding to a person. ..

FIG. 5 is a diagram illustrating a method of division processing by the heat distribution processing unit 112. As shown in FIG. 5, the heat distribution processing unit 112, for example, defines a region R cut out from the target heat distribution Img as a frontal region Pa, a trunk Pb, an arm (a part from the armpit to the wrist) Pc, The hand part Pd, the thigh part Pe, the leg part (part from the knee to the ankle) Pf, and the foot part Pg are divided into seven parts. Note that FIG. 5 shows an example in which the region R is divided into seven parts, but the number of divisions is not limited to seven.

The heat distribution processing unit 112 may be divided into a plurality of parts based on the relative positional relationship of the plurality of pixels included in the region R. For example, the heat distribution processing unit 112 divides the pixels in the lower part of the region R up to a predetermined number of pixels from the highest point (that is, the crown) as the frontal region Pa. The predetermined number of pixels is appropriately determined from the size of the region R, and is, for example, the number of pixels from the uppermost point to the lowermost point of the region R multiplied by a predetermined coefficient.

Alternatively, the heat distribution processing unit 112 may determine the portion to which each pixel of the region R belongs by comparing the region R with a standard model image of a human body.

[A2-3. Surface temperature measuring unit 114]
The surface temperature measuring unit 114 measures the surface temperature α of each part of a person including clothes from the target heat distribution Img. That is, the surface temperature measuring unit 114 includes a plurality of parts (front head Pa, trunk Pb, arm Pc, hand Pd, thigh Pe, leg Pf, foot Pg (hereinafter simply referred to as “Pa- (Also referred to as “Pg”) surface temperatures α(a) to α(g), and the measured surface temperatures α(a, b, c, d, e, f, g) are stored in the storage device 150. The temperature α indicates the skin temperature in a portion where the skin is exposed, and indicates the surface temperature of the outermost garment in a portion where the skin is exposed. Normally, the skin is exposed in the forehead Pa and the hand Pd. Therefore, in the case where only the skin of the frontal part Pa and the hand part Pd is exposed (in the case of long sleeves and long pants), the frontal part Pa and the hand part Pd measured by the surface temperature measuring part 114 are measured. The surface temperature α indicates the skin temperature of the forehead Pa and the hand portion Pd, and the surface temperature α of the other part measured by the surface temperature measurement unit 114 indicates the surface temperature of the outermost clothes.

The surface temperature measuring unit 114 extracts, for each of the plurality of parts (Pa to Pg) divided by the heat distribution processing unit 112, the temperatures of the plurality of pixels belonging to the part. The surface temperature measuring unit 114 may determine the representative temperature of the temperatures of the plurality of extracted pixels as the surface temperature α. As the representative temperature, for example, the maximum temperature of the plurality of pixels, the average temperature of all of the plurality of pixels, the average temperature of a predetermined number of higher pixels of the plurality of pixels, or the like can be used. Further, the type of representative temperature may be different for each part. For example, for the frontal part Pa and the hand part Pd where the skin is usually exposed, the maximum temperature among the plurality of pixels belonging to the part is measured as the surface temperature α, and usually for other parts to be clothed, the part is concerned. The average temperature of a plurality of pixels belonging to may be measured as the surface temperature α.

[A2-4. First estimating unit 120]
The first estimating unit 120 receives the value of the input parameter affecting the heat of the human body and outputs the value of the output parameter related to the heat of the human body, and the target heat distribution Img by using the target heat distribution Img. Estimate the condition before entering 2. The first estimation unit 120 includes a clothing amount estimation unit 122, a human body size estimation unit 124, and a condition estimation unit 126.

[A2-5. Clothing amount estimation unit 122]
The clothing amount estimation unit 122 estimates the clothing amount β of each of the plurality of parts of the person based on the temperature distribution of the region R corresponding to the person in the target heat distribution Img. The amount of clothing indicates the thermal resistance of clothing. Specifically, the clothing amount estimation unit 122 specifies the maximum temperature from the temperatures of the plurality of pixels included in the region R. The clothing amount estimation unit 122 estimates the clothing amount β based on the difference between the identified maximum temperature and the temperature of the portion of the region R corresponding to the portion, for each of the plurality of portions.

FIG. 6 is a diagram schematically showing an example of temperature change due to clothing in winter. FIG. 6 shows an example of wearing three clothes 31-33. In the example shown in FIG. 6, the surface temperature of the outermost clothes 33 is lower than the skin temperature of the human body. The difference between the skin temperature and the surface temperature of the outermost clothes 33 depends on the sum of the thermal resistance of the clothes 31 to 33 and the thermal resistance of the air layers 31' to 33'. The air layer 31 ′ is an air layer in contact with the clothes 31 and closer to the human body than the clothes 31. The air layer 32 ′ is an air layer in contact with the clothes 32 and closer to the human body than the clothes 32. The air layer 33 ′ is an air layer in contact with the clothes 33 and closer to the human body than the clothes 33.

FIG. 7 is a diagram showing an example of the correlation between the difference between the skin temperature and the surface temperature of the outermost clothes (hereinafter referred to as “surface temperature difference”) and the amount of clothes in the winter. As shown in FIG. 7, the correlation 152 indicates a relationship in which the absolute value of the surface temperature difference increases as the clothing amount increases.

The maximum temperature specified from the region R usually indicates the exposed skin temperature. The temperature of each of the plurality of parts (Pa to Pg) in the target heat distribution Img indicates the surface temperature of the outermost clothes worn on the part. The clothing amount estimation unit 122 assumes the skin temperature of the portion where the clothing is applied is the specified maximum temperature or a temperature obtained by multiplying the specified maximum temperature by a predetermined coefficient. The clothing amount estimation unit 122 calculates the clothing amount β of the portion using the difference between the assumed skin temperature and the temperature of each site in the target heat distribution Img and the correlation 152 as shown in FIG. 7. .. The correlation 152 is stored in the storage device 150 in advance.

The correlation 152 changes according to the temperature. For example, in summer, the surface temperature of clothes may be higher than the skin temperature due to the effects of outside air temperature and solar radiation. Therefore, the storage device 150 stores the correlation 152 for each temperature in advance. The clothing amount estimation unit 122 may calculate the clothing amount β using the correlation 152 corresponding to the temperature detected by the temperature/humidity sensor 53.

Alternatively, the storage device 150 stores in advance the correlation 152 at the reference temperature. The clothing amount estimation unit 122 corrects the correlation 152 according to the difference between the temperature detected by the temperature and humidity sensor 53 and the reference air temperature, and calculates the clothing amount β using the corrected correlation 152. May be.

The clothing amount estimation unit 122 estimates the clothing amounts β(a) to β(g) for a plurality of portions (Pa to Pg), respectively. The clothing amount estimation unit 122 stores the estimated clothing amount β (a, b, c, d, e, f, g) in the storage device 150.

[A2-6. Human body size estimation unit 124]
The human body size estimation unit 124 estimates the height h, weight W, and body surface area (also referred to as skin surface area) S of the person based on the size of the region R corresponding to the person in the target heat distribution Img.

FIG. 8 is a diagram illustrating a method of estimating the height by the human body size estimation unit. The human body size estimation unit 124 estimates the height h of the person according to the following equation (1).
h=N×M=N×(2d/P)×tan(θ/2)...Equation (1)
In Expression (1), d represents the distance from the heat distribution measuring device 51 to the person. The target heat distribution Img is measured at the timing when the output value from the distance measuring sensor 50 reaches a predetermined distance (for example, 3 m). Therefore, d has a constant value (for example, 3 m). θ is the angle of view of the heat distribution measuring device 51. P is the number of pixels in the vertical direction in the heat distribution. M is the length of the subject corresponding to one pixel. N is the number of pixels in the vertical direction in the region R (that is, the number of pixels from the crown to the toes).

Further, the human body size estimation unit 124 estimates the weight W and the body surface area S of the person based on the estimated height h.

FIG. 9 is a diagram showing an example of a correlation between height and standard weight. FIG. 9 shows the correlation 153 that is derived from the standard body types of the races (for example, Japanese) who enter and exit the target space 2 most frequently.

The human body size estimation unit 124 estimates the weight W of the person based on the correlation 153 and the estimated height h. Further, the human body size estimation unit 124 estimates the body surface area S(m ² ) by substituting the height h (cm) and the estimated weight W (kg) into the following equation (2).
S=100.315×(W ^0.383 ×h ^0.693 ×10 ⁻⁴ )... Formula (2)

The human body size estimation unit 124 generates human body size information γ indicating the estimated height h, weight W, and body surface area S, and stores the generated human body size information γ in the storage device 150.

[A2-7. Condition estimation unit 126]
The condition estimating unit 126 estimates the condition (for example, the value of metabolic rate) of the person before entering the target space 2 by using the human body heat model and the target heat distribution. As shown in FIG. 2, the condition estimation unit 126 has a first calculation unit 127 and a condition output unit 128.

[A2-7-1. Example of human body heat model]
The human body heat model is a mathematical model for calculating the body temperature distribution of the human body by calculating the heat balance between the human body and the environment in consideration of human body temperature regulation reactions such as sweating and blood flow. Further, the human body heat model may be a mathematical model that takes into consideration the simultaneous movement of heat and moisture in clothes. The human body heat model receives the values of a plurality of input parameters that affect the heat of the human body and outputs the values of a plurality of output parameters related to the heat of the human body. The plurality of input parameters are environmental parameters (environmental temperature, humidity, wind speed, radiant heat (including long-wavelength radiation and solar radiation)) indicating environmental conditions around a person and human body parameters (amount of clothing, Includes metabolism (activity), body weight, body surface area). The plurality of output parameters include, for example, the temperature, the heat exchange amount, and the water content of the clothes of each of the plurality of parts constituting the human body.

As the human body heat model, many models such as the Stolwijk model (see JAJ Stolwijk and JD Hardy, "Temperature Regulation in Man-A Theoretical Study" Pfluegers Archiv 291, pp129-162, 1966 (Non-patent Document 1)) and the Wisseler model It has been known. The Stolwijk model divides the human body into multiple parts and adds blood flow to these parts to perform thermal calculation.

FIG. 10: is a figure which shows an example of a human body heat model. The human body heat model shown in FIG. 10 is a model generated by reviewing the coefficients and the like based on the Stolwijk model. Specifically, the human body has eight parts, a frontal part Pa, an occipital part Ph, a trunk part Pb, an arm part Pc, a hand part Pd, a thigh part Pe, a leg part Pf, and a foot part Pg, and a central blood flow reservoir. It is divided into P18 and. Further, the frontal region Pa is divided into a region P2 which is a deep layer and a region P10 which is a skin layer. The occipital region Ph is divided into a deep layer portion P3 and a skin layer portion P11. The trunk Pb is divided into a portion P1 that is a deep layer, a portion P4 that is a muscle layer, and a portion P12 that is a skin layer. The arm portion Pc is divided into a portion P5 which is a deep layer and a portion P13 which is a skin layer. The hand portion Pd is divided into a portion P6 which is a deep layer and a portion P14 which is a skin layer. The thigh Pe is divided into a part P7 that is a deep layer and a part P15 that is a skin layer. The leg Pf is divided into a portion P8 which is a deep layer and a portion P16 which is a skin layer. The tip Pg of the foot is divided into a portion P9 which is a deep layer and a portion P17 which is a skin layer. However, the method of dividing the human body and the number of divisions are appropriately set according to the target space 2. Therefore, the human body heat model is not limited to the example shown in FIG. 10.

By solving the heat balance equation in each part Pi (i=1 to 18) using the human body heat model as shown in FIG. 10, the temperature of each part Pi (i=1 to 18) and each part Pi( The time change rate of the temperature of i=1 to 18), the amount of heat exchange (for example, heat flow on the skin surface), the amount of perspiration, etc. are calculated as output parameters. As a heat balance equation, known equations (for example, Akiko Ishiguro, Shuichi Hokoi, Akira Takada, Kyoji Ishizu, “Study on human heat model during sleep considering clothes and bedding”, Architectural Institute of Japan, Vol. 74, No. 636, pp.141-149, 2009.2").

With respect to the above human body heat model, heat and moisture transfer in clothes (see clothes 31 to 33 shown in FIG. 6) and an air layer between the clothes and the human body (see air layers 31' to 33' shown in FIG. 6). The heat and moisture simultaneous transfer model in may be applied. By applying the simultaneous heat and moisture transfer model of clothes and air layer to the human body heat model, the transfer amount of heat and water between the portion Pi, which is the skin layer of the dressed portion, and the air layer, the air layer and the adjacent layer The amount of heat and moisture transfer between the clothes and the outermost clothes and the amount of heat and moisture transfer between the outermost clothes and the ambient air are calculated. The heat and moisture simultaneous transfer model is defined by a heat balance equation and a moisture balance equation. Heat balance equation and moisture balance equation include convection temperature, radiation temperature, convection heat transfer coefficient, radiant heat transfer coefficient, absolute humidity of space (or water vapor pressure, relative humidity), humidity transfer coefficient, and skin-cloth moisture conductance. Is used. The convection temperature, the radiation temperature, and the absolute humidity of the space are set based on the measured temperature and humidity, for example. The convection heat transfer coefficient and the moisture transfer coefficient are preset based on, for example, an assumed wind speed. The skin-clothing moisture conductance is determined from the clothing amount β.

The heat-moisture simultaneous transfer model includes known techniques (for example, Akira Takada, Shuichi Hokoi, Naoki Kawakami, Masanori Kudo, “Unsteady thermophysiological response of human body considering heat and water transfer and accumulation in clothes-subject. Experiment and analysis using TWO-node model-", Proceedings of Architectural Institute of Japan, No. 549, pp.20-30, 2001.11") can be used.

By applying the heat-moisture transfer model to the human body heat model, the output parameter of the human body heat model has a surface temperature T _cloth (T _cloth (T _cloth ) of the portion of the plurality of portions (Pa to Ph) where the clothing amount β is not 0. a) to T _close (h) and water content are included.

[A2-7-2. First computing unit]
The temperature of each part when the person enters the target space 2 varies depending on the environmental conditions around the person and the human body side conditions until the person enters the target space 2. For example, the temperature of each part is different between the person who was in the vehicle in the air-conditioned state immediately before entering the target space 2 and the person who was in the hot outside air immediately before entering the target space 2. Further, the temperature of each part is different between the person who was running just before entering the target space 2 and the person who was in the sitting posture in the vehicle immediately before entering the target space 2.

The first calculation unit 127 performs, for each of the plurality of virtual patterns, an input process of inputting the value of the input parameter corresponding to the virtual pattern to the human body heat model, and a plurality of parts (Pa to Ph) of the person including clothes. The surface temperatures α′(a) to α′(h) are calculated respectively.

Specifically, the first calculation unit 127 uses the human body heat model shown in FIG. For each of the plurality of virtual patterns, the first calculation unit 127 inputs the value of the input parameter corresponding to the virtual pattern into the human body heat model, and the temperature T _{i of} each part Pi (i=1 to 18) and the clothing The temperatures T _close (a) to T _close (h) and the water content of the clothes are calculated. The first calculation unit 127 performs calculation using the human body heat model on the assumption that a person in a predetermined reference state has entered the target space 2 after spending a prescribed time (for example, 10 minutes) in a virtual pattern. Do. That is, the first calculation unit 127 sets a predetermined value corresponding to the reference state as the initial temperature of each part Pi (i=1 to 18), the initial temperature of the clothes, and the water content of the clothes. Then, the first calculation unit 127 determines the temperature T _i of each site Pi (i=1 to 18) after a lapse of a specified time under the conditions of the virtual pattern, and the wearing amount β of the plurality of parts (Pa to Ph). The surface temperatures T _cloth (a) to T _cloth (h) of the portion of the clothing where _N is not 0 are calculated.

The plurality of virtual patterns have different values for at least one variation parameter. The variation parameter includes, for example, at least one of the ambient temperature of the person and the metabolic rate of the human body. For example, when it is assumed that there is a difference in the environment before entering the target space 2, the plurality of virtual patterns have different values for the temperature that is the variation parameter. Further, when it is assumed that there is a difference in the amount of exercise until entering the target space 2 depending on the person, the plurality of virtual patterns have different values for the metabolic amount, which is a variation parameter.

The first calculation unit 127 may use a common value for a plurality of virtual patterns for input parameters other than the fluctuation parameter among the plurality of input parameters. Specifically, the first calculation unit 127 uses the value of the clothing amount β estimated by the clothing amount estimation unit 122 in all of the plurality of virtual patterns. The first calculation unit 127 uses the values of the weight W and the body surface area S estimated by the human body size estimation unit 124 in all of the plurality of virtual patterns. Further, the first calculation unit 127 uses the values of the humidity, the wind speed, and the radiant heat amount that are determined according to the environment outside the target space 2 in all of the plurality of virtual patterns. For example, the first calculation unit 127 may use the humidity measurement value detected by the temperature/humidity sensor 53. The first calculation unit 127 may use the wind speed and the radiant heat amount determined from the weather information distributed from the external server in all of the plurality of virtual patterns. Furthermore, when the variation parameter does not include the ambient temperature of the person, the first calculation unit 127 determines the value of the temperature (for example, the temperature and humidity) determined according to the environment outside the target space 2 in all of the plurality of virtual patterns. The temperature measurement value detected by the sensor 53) may be used.

The first computing unit 127 computes the temperature of the skin layer portion of the portion where the clothing amount β is 0 among the plurality of portions (Pa to Ph) as the surface temperature α′ of the portion. The first calculation unit 127 calculates the temperature of the outermost clothes in the portion where the amount of clothing β is not 0 among the plurality of portions (Pa to Ph) as the surface temperature α′ of the portion.

[A2-7-3. Virtual pattern]
FIG. 11 is a diagram showing an example of the surface temperatures α′ of a plurality of portions (Pa to Pg) calculated for each virtual pattern. In the example shown in FIG. 11, the surface temperature α′ is calculated for each of 15 virtual patterns in which at least one of temperature and metabolism is different. The temperature is one of an outside air temperature T1 detected by the temperature/humidity sensor 53, an air temperature T1+d1 higher than the outside air temperature T1 by a predetermined value d1, and an air temperature T1-d2 lower than the outside air temperature T1 by a predetermined value d2. The amount of metabolism includes the amount of metabolism when sitting on a seat such as in a car, the amount of metabolism when walking, the amount of metabolism when walking lightly (fast walking), the amount of metabolism when performing light exercise (biking), and the amount of exercise (running). ) Is the amount of metabolism.

In FIG. 11, 3×5=15 virtual patterns generated by selecting one of the three conditions as the ambient temperature of the person and one of the five conditions as the metabolism amount. Is shown. However, the number of conditions prepared as the ambient temperature of the person is not limited to three, and may be two or four or more. Similarly, the number of conditions prepared as the metabolic amount is not limited to 5, and may be 2 to 4 or 6 or more.

[A2-7-4. Condition output part]
The condition output unit 128 targets the person based on the comparison result of the surface temperature α′ of the plurality of portions (Pa to Pg) calculated by the first calculation unit 127 and the temperature distribution of the region R in the target heat distribution Img. The condition of the person before entering the space 2 is estimated.

Specifically, the condition output unit 128 corresponds to the surface temperature α′ closest to the surface temperature α of the plurality of portions (Pa to Pg) measured by the surface temperature measurement unit 114 from the plurality of virtual patterns. Extract a virtual pattern. The condition output unit 128 estimates the value of the metabolic rate corresponding to the extracted virtual pattern as the value of the metabolic rate of the person before entering the target space 2. Furthermore, the condition output unit 128 estimates the temperature value corresponding to the extracted virtual pattern as the temperature value around the person before entering the target space.

The condition output unit 128 is obtained by inputting the values of the fluctuation parameter (for example, temperature and metabolic rate) and the environmental parameter (temperature, humidity, wind speed, and radiant heat amount) corresponding to the extracted virtual pattern into the human body heat model. The condition information δ including the value of the output parameter is output to the second estimation unit 140. The value of the output parameter included in the condition information δ is information indicating the condition of the person when entering the target space 2, and the temperature of each part Pi (i=1 to 18) and each part Pi (i=1 to 18). ) Temperature change rate, heat exchange amount (for example, skin surface heat flow), perspiration accumulation amount, and water content of clothes.

[A2-8. Second estimation unit 140]
The second estimating unit 140 estimates the temporal change in the thermal sensation of the person after entering the target space 2 using the human body heat model.

FIG. 12 is a diagram showing an example of a temporal change in a person's thermal sensation after entering the target space 2. As shown in FIG. 12, the thermal sensation of the person after entering the target space 2 may change with time. This variation depends on the temperature of each part Pi when entering the target space 2, and the environmental conditions of the target space 2 and the conditions on the human body side. The second estimation unit 140 stores, in the storage device 150, the thermal sensation ε after a lapse of a predetermined time (for example, 20 minutes) after entering the target space 2 in consideration of the average staying time of the target space 2. ..

As shown in FIG. 2, the second estimation unit 140 includes a second calculation unit 142 and a thermal sensation prediction unit 144.

[A2-8-1. Second operation unit 142]
The second calculation unit 142 sets the value of the output parameter indicated by the condition information δ as an initial value, and inputs the values of the plurality of input parameters after entering the target space 2 to the human body heat model, thereby making the target space 2 The value of the output parameter after entering is calculated. The second calculation unit 142 calculates the value of the output parameter for each elapsed time after entering the target space 2.

The values of the plurality of input parameters after entering the target space 2 are the clothing amount β estimated by the clothing amount estimation unit 122, the weight W and the body surface area S estimated by the human body size estimation unit 124, and the target space 2 And the value of the metabolic rate determined from the physical activity (for example, light walking) assumed within.

The values of the plurality of input parameters after entering the target space 2 further include values of temperature, humidity, wind speed, and radiant heat that are determined according to the environment in the target space 2. The values of temperature and humidity are determined from the measured values of the temperature/humidity sensor 52. The values of wind speed and radiant heat are determined in advance by experiments or simulations.

The temperature around the person may change depending on the height from the floor. Therefore, the temperature/humidity sensor 52 preferably measures the temperature Tp near the floor in the target space 2 and the temperature Tq at the position of the reference height H from the floor. The reference height H is, for example, 2 m. The second calculation unit 142 uses the height h included in the human body size information γ, the temperatures Tp and Tq, and the reference height H to determine the ambient temperature of each of the plurality of portions (Pa to Ph) of the person. Just decide. For example, the temperature Ta around the forehead Pa and the occipital region Ph, the temperature Tb around the trunk Pb and the arm Pc, the temperature Td of the hand Pd and the thigh Pe, the temperature Tf of the leg Pf, and the tip of the foot. The temperature Tg of Pg is determined according to the following equations (3-1) to (3-5).
_{Ta = Tp + k a × (} Tq-Tp) × h / H ··· formula (3-1)
_{Tb = Tp + k b × (} Tq-Tp) × h / H ··· formula (3-2)
_{Td = Tp + k d × (} Tq-Tp) × h / H ··· formula (3-3)
_{Tf = Tp + k f × (} Tq-Tp) × h / H ··· formula (3-4)
Tg=Tp... Formula (3-5)
Note that the coefficient k _a, with respect to height, ratio of the height from the standard floor head center is set. The coefficient k _b, for height, the height ratio of from standard floor of the trunk center is set. For the coefficient k _d , the ratio of the height from the standard floor surface of the center of the hand to the height is set. The coefficient k _f, for height, the ratio of the height of the standard floor leg center is set.

The output parameters calculated by the second calculator 142 include the temperature of each part Pi (i=1 to 18), the amount of heat exchange (for example, heat flow on the skin surface), the amount of perspiration accumulation, the water content of clothes, and the like. ..

[A2-8-2. Thermal sensation prediction unit 144]
The thermal sensation prediction unit 144 acquires the value of the output parameter calculated by the second calculation unit 142. The thermal sensation prediction unit 144 predicts the thermal sensation of a person based on the value of the acquired output parameter. Specifically, the thermal sensation prediction unit 144 predicts the thermal sensation ε after entering the target space 2 by inputting the acquired value of the output parameter into a known thermal sensation prediction formula.

As the thermal sensation prediction formula, for example, the following formulas (4-1) to (4-5) are known. Equation (4-1) is "Hui Zhang, Edward Arens, Charlie Huizenga, Taeyoung Han, "Thermal sensation and comfort models for non-uniform and transient environments: Part I: Local sensation of individual body parts", Building and Environment Volume. 45, Issue 2, Pages 380-388, 2010.2". Formulas (4-2) and (4-3) are "Satoru Takada, Sho Matsumoto, Takayuki Matsushita, "Prediction of whole-body thermal sensation in the non-steady state based on skin temperature", Building and Environment Volume 68, Pages. 123-133, 2013.10”. The formula (4-4) is “Ikue Mori, Shuichi Hokoi, Akira Takada, Hiroaki Tanaka, “Experimental consideration of prediction of thermal sensation in non-steady state”, Proceedings of Architectural Institute of Japan, No.563, pp. .9-15, 2003.1”. Formula (4-5) is disclosed in "Japan Society for Air Conditioning and Sanitary Engineering, "New Edition, A Mechanism of Comfortable Thermal Environment, Aiming at Rich Living Space," Maruzen, 2006.

The thermal sensation prediction unit 144 may extract the value of the parameter used in the thermal sensation prediction formula from the acquired values of the output parameters, and input the extracted parameter value to the thermal sensation prediction formula. For example, when using a thermal sensation prediction formula (for example, formula (4-2)) that predicts thermal sensation from the temperature of a part, the thermal sensation prediction unit 144 uses the output parameters calculated by the second calculation unit 142. Of the values, the temperature of the site after a predetermined time has passed since the person entered the target space 2 may be used. When using a thermal sensation prediction formula that predicts thermal sensation from the rate of temperature change of the part, the thermal sensation prediction unit 144 changes the temperature of the part from the value of the output parameter for each elapsed time after entering the target space 2. The rate may be calculated and the rate of temperature change may be input to the thermal sensation prediction formula.

[A2-9. Storage device 150]
The storage device 150 stores various programs and various data. For example, the storage device 150 stores, for each target heat distribution Img, history data in which measurement time, surface temperature α, clothing amount β, human body size information γ, condition information δ, and thermal sensation ε are associated with each other. 151 is stored.

[A2-10. Distribution generation unit 160]
Next, with reference to FIG. 13, a method of generating the population distribution 162 by the distribution generation unit 160 will be described. FIG. 13 is a diagram showing a specific example of the distribution of people generated by the distribution generation unit.

The distribution generation unit 160 uses the history of the thermal sensation estimated by the second estimation unit 140 every time the person enters the target space 2 and the heat distribution acquisition unit 110 acquires the target heat distribution Img of the person, and then the temperature is cooled. A distribution 162 of people for each feeling is generated.

Typically, the distribution generation unit 160 specifies the thermal sensation ε calculated from the present to the past certain period (for example, one hour) in the history data 151, and uses the thermal sensation ε to distribute the number of people. 162 is generated. As a result, the number distribution 162 of the thermal sensation ε from the present to the past fixed period is generated. The generated number distribution 162 is output to the support information generation unit 170.

[A2-11. Support information generation unit 170]
The support information generation unit 170 generates support information 172 for supporting the control of the air conditioner 200 based on the distribution 162 of people. The support information 172 is information for making the peak of the distribution of people 162 closer to “0: neutral”, and indicates recommended values such as temperature, humidity, air volume, and the number of operating machines. The generated support information 172 is output to the air conditioning control unit 180.

For example, in the case of the number distribution 162 as shown in FIG. 13, the support information generation unit 170 generates support information 172 that recommends a preset temperature lower than the current preset temperature by a predetermined value for the indoor unit 200A. Alternatively, the support information generation unit 170 may generate support information 172 that recommends a blower volume of the blower 200D that is larger than the current blower volume by a predetermined value. The predetermined value is determined according to the peak value of the population distribution 162.

[A2-12. Air conditioning control unit 180]
The air conditioning controller 180 refers to the support information 172 to control the air conditioner 200. For example, the air conditioning control unit 180 controls the set temperature of the indoor unit 200A. Alternatively, the air conditioning control unit 180 may control the rotation speed or on/off of the ventilation device.

[A3. Processing flow of information processing apparatus 100]
The processing flow of the information processing apparatus 100 will be described with reference to FIGS. 14 and 15. FIG. 14 is a flowchart showing the flow of processing for generating the above-mentioned history data 151 (see FIG. 2). FIG. 15 is a flowchart showing the flow of processing for controlling the air conditioner 200 using the generated history data 151. The processing illustrated in FIGS. 14 and 15 is realized by the information processing apparatus 100 executing a program. In other aspects, some or all of the processing may be performed by circuit elements or other hardware.

[A3-1. Generation flow of detection history]
First, a generation flow of the history data 151 will be described with reference to FIG. In step S1, the information processing apparatus 100 determines whether or not a person has entered the target space 2 based on the change in the output value of the distance measuring sensor 50. This determination method is [A2-1. The heat distribution acquisition unit 110] is the same as that described above, and the description thereof will not be repeated. When it is determined that the person has entered the target space 2 (YES in step S1), the information processing apparatus 100 executes the process of step S2. If not (NO in step S1), the information processing apparatus 100 executes step S1 again.

In step S2, the information processing apparatus 100 acquires the target heat distribution Img from the heat distribution measuring device 51 and performs processing on the target heat distribution Img.

In step S3, the information processing apparatus 100 measures the surface temperature α of each of the plurality of portions (Pa to Pg) based on the target heat distribution Img. The measured surface temperature α is stored in the storage device 150.

In step S4, the information processing apparatus 100 estimates the clothing amount β of each of the plurality of portions (Pa to Pg) based on the target heat distribution Img. The estimated clothing amount β is stored in the storage device 150.

In step S5, the information processing apparatus 100 estimates a human body size (height h, weight W, and body surface area S) based on the target heat distribution Img, and generates human body size information γ. The generated human body size information γ is stored in the storage device 150.

In step S<b>6, the information processing apparatus 100 uses the human body heat model and the surface temperature α measured from the target heat distribution Img to change the variation parameters (for example, metabolic rate and ambient temperature) before the person enters the target space 2. ) Is estimated. The condition information δ including the estimated value of the variation parameter is stored in the storage device 150.

In step S7, the information processing apparatus 100 inputs the value of the input parameter estimated in steps S4 to 6 (hereinafter, referred to as “first value of input parameter”) to the human body heat model to obtain the value of the output parameter. Condition information δ including (hereinafter, referred to as “first value of output parameter”) is generated.

In step S8, the information processing apparatus 100 sets the value of the output parameter indicated by the condition information δ as an initial value, and the value of the input parameter after entering the target space 2 (hereinafter, referred to as “second value of input parameter”). Is input to the human body heat model to calculate the value of the output parameter (hereinafter referred to as the “second value of the output parameter”).

In step S9, the information processing apparatus 100 estimates a person's thermal sensation ε based on the second value of the output parameter calculated in step S8. The estimated thermal sensation ε is stored in the storage device 150.

Thus, every time a person enters the target space 2, steps S2 to S9 are repeated. As a result, history data 151 in which the measurement time, the surface temperature α, the clothing amount β, the human body size information γ, the condition information δ, and the thermal sensation ε are associated with each other is stored in the storage device 150.

[A3-2. Air conditioning control flow]
Next, an air conditioning control flow using the history data 151 will be described with reference to FIG. In step S11, the information processing device 100 determines whether or not the specified timing has arrived. As an example, the specified timing arrives at a predetermined cycle (for example, every hour). The period may be set in advance, or may be arbitrarily set by the user. When determining that the specified timing has come (YES in step S11), information processing apparatus 100 executes step S12. When that is not right (it is NO at step S11), the information processing apparatus 100 performs step S11 again.

In step S<b>12, the information processing apparatus 100 generates the number distribution 162 (see FIG. 12) for each thermal sensation based on the history data 151.

In step S<b>13, the information processing device 100 generates the support information 172 for supporting the control of the air conditioner 200, based on the distribution of people 162.

In step S14, the information processing device 100 controls the air conditioning device 200 based on the generated support information 172.

[A4. Hardware configuration of information processing apparatus 100]
An example of the hardware configuration of the information processing device 100 will be described with reference to FIG. FIG. 16 is a block diagram showing the main hardware configuration of the information processing device 100. As shown in FIG. 16, the information processing apparatus 100 includes a control device 101, a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 103, a communication interface 104, a display interface 105, and an input interface 107. And a storage device 150.

The control device 101 controls the information processing device 100. The control device 101 is composed of, for example, at least one integrated circuit. The integrated circuit is configured by, for example, at least one CPU (Central Processing Unit), at least one ASIC (Application Specific Integrated Circuit), at least one FPGA (Field Programmable Gate Array), or a combination thereof.

The control device 101 controls the information processing device 100 by executing various programs such as the information processing program 155 of the information processing device 100. The control device 101 reads the information processing program 155 from the storage device 150 to the ROM 102 based on the reception of the execution instruction of the information processing program 155. The RAM 103 functions as a working memory and temporarily stores various data necessary for executing the information processing program 155.

The information processing apparatus 100 exchanges data with an external communication device via the communication interface 104. The external communication devices include, for example, a distance measuring sensor 50 (see FIG. 1), a heat distribution measuring device 51 (see FIG. 1), temperature/humidity sensors 52 and 53 (see FIG. 1), an operation panel 54 (see FIG. 1), It includes an air conditioner 200 (see FIG. 1), a server (not shown), other communication terminals, and the like. Communication between the communication terminal and the information processing device 100 may be realized by wireless communication or wire communication.

A display 106 may be connected to the display interface 105. The display interface 105 is, for example, an HDMI (High-Definition Multimedia Interface) (registered trademark) terminal or a VGA (Video Graphics Array) terminal. The display interface 105 sends an image signal for displaying an image to the display 106 according to a command from the control device 101 or the like. The display 106 is, for example, a liquid crystal display, an organic EL display, or another display device.

An input device 108 can be connected to the input interface 107. The input interface 107 is, for example, a USB (Universal Serial Bus) terminal. The input interface 107 receives a signal indicating a user operation from the input device 108. The input device 108 is, for example, a mouse, a keyboard, a touch panel, or any other device capable of receiving a user operation.

The storage device 150 is, for example, a storage medium such as a hard disk or an external storage device. The storage device 150 stores the information processing program 155 of the information processing device 100, the history data 151 (see FIG. 2), the correlation 152 (see FIG. 7), the correlation 153 (see FIG. 12), and the like. To do. These storage locations are not limited to the storage device 150, and may be stored in the storage area of the control device 101 (for example, cache memory or the like), the ROM 102, the RAM 103, an external device (for example, the server or the air conditioning device 200), or the like. Good.

The information processing program 155 may be provided as a part of an arbitrary program instead of as a stand-alone program. In this case, the process according to the present embodiment is realized in cooperation with any program. Even a program that does not include some of such modules does not depart from the spirit of the information processing program 155 according to the present embodiment. Furthermore, some or all of the functions provided by the information processing program 155 may be realized by dedicated hardware. Furthermore, the information processing apparatus 100 may be configured in a form such as a so-called cloud service in which at least one server executes a part of the processing of the information processing program 155.

<B. Embodiment 2>
In the above-described first embodiment, the clothing amount β is estimated based on the temperature distribution of the region R in the target heat distribution Img. However, the clothing amount β may be estimated by the condition estimation unit as one of the variation parameters.

FIG. 17 is a diagram illustrating an example of a functional configuration of the information processing device according to the second embodiment. The information processing apparatus 100A according to the second embodiment differs from the information processing apparatus 100 shown in FIG. 2 in that a first estimating unit 120A is provided instead of the first estimating unit 120. The first estimating unit 120A is different from the first estimating unit 120 shown in FIG. 2 in that a clothing form estimating unit 123 and a condition estimating unit 126A are provided instead of the clothing amount estimating unit 122 and the condition estimating unit 126. .. The condition estimation unit 126A differs from the condition estimation unit 126 shown in FIG. 2 in that it includes a first calculation unit 127A and a condition output unit 128A instead of the first calculation unit 127 and the condition output unit 128.

The clothing form estimation unit 123 estimates the form of clothes worn by a person based on the temperature distribution of the region R of the target heat distribution Img. The clothing form estimation unit 123 outputs the estimation result to the first calculation unit 127A.

For example, when the surface temperature of the arm Pc is closer to the surface temperature of the forehead Pa than the surface temperature of the trunk Pb, the clothing form estimation unit 123 may estimate that the form of the clothes is “short sleeve”. .. Conversely, if the surface temperature of the arm Pc is closer to the surface temperature of the trunk Pb than the surface temperature of the forehead Pa, the clothes form estimation unit 123 estimates that the form of the clothes is “long sleeve”. Good.

Furthermore, when the surface temperature of the legs Pf is closer to the surface temperature of the frontal region Pa than the surface temperature of the trunk Pb, the clothing form estimation unit 123 determines that the form of the clothes is “short pants (or short skirt)”. It can be estimated that there is. Conversely, when the surface temperature of the arm Pc is closer to the surface temperature of the trunk Pb than the surface temperature of the forehead Pa, the clothes form estimating unit 123 determines that the form of the clothes is “long pants (or long skirt)”. It can be estimated that

Similarly to the above-described first calculation unit 127, the first calculation unit 127A inputs the value of the input parameter corresponding to the virtual pattern into the human body heat model for each of the plurality of virtual patterns, and the plurality of parts (Pa ~ The surface temperatures α′(a) to α′(h) of Ph) are calculated. As described above, the plurality of virtual patterns have different values for at least one variation parameter. In the second embodiment, the variation parameter includes at least the clothing amount.

For example, the variation parameter includes the amount of clothing and the amount of metabolism. When the number of values estimated as the clothing amount is N1 and the number of values estimated as the metabolic amount is N2, N1×N2 virtual patterns are generated in advance.

Alternatively, the variation parameter includes the amount of clothing, the amount of metabolism, and the temperature around the person. When the number of values assumed as the amount of clothing is N1, the number of values expected as the amount of metabolism is N2, and the number of values that can be taken as the ambient temperature is N3, N1×N2× N3 virtual patterns are generated in advance.

FIG. 18: is a figure which shows seven clothing patterns A1-A7 assumed in the summer. FIG. 19 is a diagram showing eight clothing patterns B1 to B8 assumed in the intermediate period (spring and autumn). FIG. 20: is a figure which shows eight clothing patterns C1-C8 assumed in winter. The number of clothing patterns is not limited to 7 or 8, and may be 2 to 6 or 8 or more.

In the case of summer, each of the plurality of virtual patterns corresponds to the clothing amount of any of the clothing patterns A1 to A7 shown in FIG. In the middle period, each of the plurality of virtual patterns corresponds to the wearing amount of any of the wearing patterns B1 to B8 shown in FIG. In the winter season, each of the plurality of virtual patterns corresponds to the clothing amount of any of the clothing patterns C1 to C8 shown in FIG. Note that the year is not limited to being divided into three periods, that is, the summer period, the intermediate period, and the winter period. For example, the interim period may be divided into first to third interim periods, and one year may be divided into five periods. In the case of the first intermediate period, each of the plurality of virtual patterns may correspond to the clothing amount of any of the clothing patterns A1 to A7 shown in FIG. 18 and the clothing patterns B1 to B8 shown in FIG. In the second intermediate period, each of the plurality of virtual patterns may correspond to the clothing amount of any of the clothing patterns B1 to B8 shown in FIG. In the case of the third intermediate period, each of the plurality of virtual patterns may correspond to the clothing amount of any of the clothing patterns B1 to B8 shown in FIG. 19 and the clothing patterns C1 to C8 shown in FIG. The determination of which period the present belongs to is made, for example, by using the present date, the outside temperature, and the like.

When the number of virtual patterns is large, the calculation time of the first calculation unit 127A becomes long. Therefore, the first calculation unit 127A reduces the number of virtual patterns based on the estimation result of the clothing form estimation unit 123. For example, when the estimation result indicates “short sleeve”, the first calculation unit 127A excludes the virtual pattern corresponding to the clothing pattern including “long sleeve” from the calculation target. When the estimation result indicates “short pants”, the first calculation unit 127A excludes the virtual pattern corresponding to the clothing pattern including “long pants” from the calculation target.

127 A of 1st calculating parts input the value of the input parameter corresponding to the said virtual pattern about each of the several virtual pattern used as a calculation object, and the surface temperature of several parts (Pa-Ph). α′(a) to α′(h) are calculated respectively.

The first calculation unit 127A may use common values for a plurality of virtual patterns for input parameters other than the variation parameters (for example, the amount of clothing, the amount of metabolism, and the temperature around the person) among the plurality of input parameters. Specifically, the first calculation unit 127A uses the weight W and the body surface area S estimated by the human body size estimation unit 124 in all of the plurality of virtual patterns. Moreover, 127 A of 1st calculating parts use the value of the humidity, wind speed, and radiant heat which are set according to the environment outside the target space 2 in all of several virtual patterns. For example, the first calculation unit 127A may use the humidity measurement value detected by the temperature/humidity sensor 53. The first calculation unit 127A may use the wind speed and the radiant heat amount determined from the weather information distributed from the external server in all of the plurality of virtual patterns. Furthermore, when the variation parameter does not include the ambient temperature of the person, the first computing unit 127A determines the value of the temperature (for example, the temperature and humidity) determined according to the environment outside the target space 2 in all of the plurality of virtual patterns. The temperature measurement value detected by the sensor 53) may be used.

The condition output unit 128A targets the person based on the comparison result of the surface temperature α′ of the plurality of portions (Pa to Pg) calculated by the first calculation unit 127A and the temperature distribution of the region R in the target heat distribution Img. The condition of the person before entering the space 2 is estimated.

Specifically, the condition output unit 128A corresponds to the surface temperature α′ closest to the surface temperature α of the plurality of portions (Pa to Pg) measured by the surface temperature measurement unit 114 from the plurality of virtual patterns. Extract a virtual pattern. The condition output unit 128A estimates the value of the clothing amount corresponding to the extracted virtual pattern as the value of the clothing amount of the person who enters the target space 2. The condition output unit 128A estimates the value of the metabolic rate corresponding to the extracted virtual pattern as the value of the metabolic rate of the person before entering the target space 2. Furthermore, the condition output unit 128A estimates the temperature value corresponding to the extracted virtual pattern as the temperature value around the person before entering the target space.

The condition output unit 128A inputs the values of fluctuation parameters (eg, clothing amount, ambient temperature and metabolic rate) and environmental parameters (temperature, humidity, wind speed and radiant heat amount) corresponding to the extracted virtual pattern into the human body heat model. The condition information δ including the value of the output parameter obtained by performing the above is output to the second estimation unit 140. Further, the condition output unit 128A includes the estimated clothing amount β in the condition information δ.

As described above, according to the second embodiment, the surface temperatures α′ of the plurality of portions (Pa to Pg) obtained by inputting each of the plurality of assumed clothing amount values into the human body heat model, The surface temperatures α of a plurality of portions (Pa to Pg) measured from the target heat distribution Img are compared. Then, the amount of clothing corresponding to the surface temperature α′ closest to the surface temperature α is estimated as the amount of clothing of the person who enters the target space 2. This improves the accuracy of estimating the amount of clothing of a person.

<C. Modification>
[C1. Modification 1]
In the above description, the information processing device 100 includes the air conditioning control unit 180. However, the air conditioning control unit 180 may be included in the air conditioning device 200. In this case, the air conditioner 200 acquires the support information 172 from the information processing device 100 and performs control based on the support information 172.

[C2. Modification 2]
The information processing devices 100 and 100A may include an output unit that outputs the support information 172 to the external display 106 (see FIG. 15) instead of the air conditioning control unit 180. Alternatively, the information processing devices 100 and 100A may include an output unit that outputs the support information 172 to an external terminal device connected by communication, instead of the air conditioning control unit 180. Thus, the person in charge of air conditioning management of the target space 2 can confirm the support information 172 displayed on the display 106 or the terminal device and control the air conditioner 200 according to the support information 172. As a result, even in the target space 2 in which a specific number of persons enter and leave, the environment in the target space 2 is comfortably maintained according to the state of the persons.

[C3. Modification 3]
When the entrance/exit 4 is dedicated to the entrance, the heat distribution acquisition unit 110 determines the heat distribution measured by the heat distribution measuring device 51 when the output value of the distance measuring sensor 50 reaches a predetermined distance as the target heat distribution Img. You can get it as

[C4. Modification 4]
The correlation 153 is created in advance based on the standard body type. Therefore, the first calculation unit 127 and the second calculation unit 142 may not be able to accurately calculate the value of the output parameter for a person whose difference from the standard body type is large. Therefore, the surface temperature measuring unit 114, the clothing amount estimating unit 122 (or the clothing form estimating unit 123), and the human body size estimating unit 124 process only the target heat distribution Img in which the size (the number of pixels) of the region R is within the predetermined range. May be That is, the target heat distribution Img in which the size of the region R is outside the predetermined range may be excluded from the processing target. For example, the target heat distribution Img of an extremely fat person and the target heat distribution Img of a child may be excluded from the processing targets.

<D. Summary>
As described above, the information processing devices 100 and 100A include the heat distribution acquisition unit 110 that acquires the target heat distribution Img generated by the heat distribution measuring device 51 measuring the person entering the target space 2. The information processing apparatuses 100 and 100A use the human body heat model that outputs the value of the output parameter related to the heat of the human body in response to the value of the input parameter that influences the heat of the human body, and the target heat distribution, so that the person is in the target space. The first estimating unit 120, 120A for estimating the first value of the input parameter before entering 2 is provided. The first estimation unit 120 includes a clothing amount estimation unit 122, a human body size estimation unit 124, and a condition estimation unit 126. The first estimation unit 120A includes a clothing form estimation unit 123, a human body size estimation unit 124, and a condition estimation unit 126A. The information processing devices 100 and 100A include a second estimation unit 140 as a second estimation unit that estimates the thermal sensation of the person after entering the target space 2 using the human body heat model. Each of the information processing devices 100 and 100A accumulates the thermal sensation ε estimated by the second estimation unit 140 in a predetermined period every time the thermal distribution acquisition unit 110 acquires the target thermal distribution, and the thermal sensation is calculated from the accumulated result for each thermal sensation. The distribution generation unit 160 for generating the number distribution 162 of The information processing devices 100 and 100A include a support information generation unit 170 for generating support information for supporting control of the air conditioner 200 based on the distribution of the number of people. The second estimation unit 140 sets the first value of the output parameter obtained by inputting the first value of the input parameter estimated by the first estimation unit into the human body heat model as an initial value, and then enters the target space 2. The second value of the output parameter is calculated by inputting the second value of the input parameter of 1 to the human body heat model. The second estimation unit 140 estimates the thermal sensation of the person based on the second value of the output parameter.

According to the above configuration, the information processing apparatus 100 or 100A can estimate the condition of the person before entering the target space 2, and can estimate the thermal sensation after entering the target space 2 in consideration of the condition. That is, the thermal sensation according to the state of the person is estimated. Then, the support information 172 for controlling the air conditioner 200 is generated based on the distribution of the number of people 162 of the thermal sensation. The thermal sensation is estimated based on the heat distribution of the person who enters the target space 2. Therefore, even in the target space 2 in which an unspecified number of people enter and leave, excessive cooling and heating are suppressed by referring to the support information 172, and the power consumption of the air conditioner 200 is reduced.

The embodiments disclosed this time are to be considered as illustrative in all points and not restrictive. The scope of the present invention is shown not by the above description but by the claims, and is intended to include meanings equivalent to the claims and all modifications within the scope.

DESCRIPTION OF SYMBOLS 1 air conditioning system, 2 object space, 4 entrance/exit, 31-33 clothes, 31'-33' air layer, 50 distance measuring sensor, 51 heat distribution measuring device, 52, 53, 53A, 53B temperature/humidity sensor, 54 operation panel, 100,100A Information processing device, 101 control device, 102 ROM, 103 RAM, 104 communication interface, 105 display interface, 106 display, 107 input interface, 108 input device, 110 heat distribution acquisition part, 112 heat distribution processing part, 114 surface Temperature measuring unit, 120, 120A first estimating unit, 122 clothing amount estimating unit, 123 clothing form estimating unit, 124 human body size estimating unit, 126, 126A condition estimating unit, 127, 127A first calculating unit, 128, 128A condition output Part, 140 second estimation part, 142 second calculation part, 144 thermal sensation prediction part, 150 storage device, 151 history data, 152, 153 correlation, 155 information processing program, 160 distribution generation part, 162 number distribution, 170 Support information generation part, 172 support information, 180 air conditioning control part, 200 air conditioner, 200A indoor unit, 200B ventilation device, 200C outdoor unit, 200D blower.

Claims (10)

An acquisition unit for acquiring a target heat distribution generated by the heat distribution measuring device measuring a person entering the target space,
By using the human body heat model which outputs the value of the output parameter relating to the heat of the human body in response to the value of the input parameter affecting the heat of the human body and the target heat distribution, the person before the person enters the target space A first estimator for estimating a first value of the input parameter;
A second estimation unit for estimating the thermal sensation of the person after entering the target space using the human body heat model;
Distribution generation for accumulating the thermal sensation estimated by the second estimating unit each time the acquisition unit acquires the target heat distribution in a predetermined period, and generating a distribution of people for each thermal sensation from the accumulation result. Department,
An information generation unit for generating support information for supporting control of the air conditioner based on the distribution of the number of people,
The second estimation unit is
The first value of the output parameter obtained by inputting the first value of the input parameter estimated by the first estimation unit into the human body heat model is set as an initial value, and the first value of the output parameter after entering the target space The second value of the output parameter is calculated by inputting the second value of the input parameter to the human body heat model,
An information processing apparatus for estimating the thermal sensation of the person based on the second value of the output parameter. The input parameters include the amount of clothing, the amount of metabolism, the weight and the surface area of the person,
The output parameters include surface temperatures of portions of the person, including clothing,
The first estimation unit is
Based on the size of the region corresponding to the person in the target heat distribution, to estimate the value of the weight and body surface area of the person,
Based on the temperature distribution of the region in the target heat distribution, to estimate the value of the amount of clothing of the person,
The estimated body weight, body surface area, and clothing amount value, and the metabolic rate value corresponding to each of a plurality of virtual patterns are input to the human body heat model. Compare with the temperature distribution of the area,
From the plurality of virtual patterns, extract a virtual pattern corresponding to the surface temperature of the plurality of portions closest to the temperature distribution of the region,
The information processing apparatus according to claim 1, wherein the value of the metabolic rate corresponding to the extracted virtual pattern is estimated as the value of the metabolic rate of the person before entering the target space. The input parameters include the amount of clothing, the amount of metabolism, the weight and the surface area of the person,
The output parameters include surface temperatures of portions of the person, including clothing,
The first estimation unit is
Based on the size of the region corresponding to the person in the target heat distribution, to estimate the value of the weight and body surface area of the person,
Surfaces of the plurality of portions obtained by an input process of inputting estimated body weight and body surface area values, and clothing amount values and metabolic rate values corresponding to each of a plurality of virtual patterns to the human body heat model Compare the temperature and the temperature distribution of the region,
From the plurality of virtual patterns, extract a virtual pattern corresponding to the surface temperature of the plurality of portions closest to the temperature distribution of the region,
Estimating the value of the clothing amount corresponding to the extracted virtual pattern as the value of the clothing amount of the person,
The information processing apparatus according to claim 1, wherein the value of the metabolic rate corresponding to the extracted virtual pattern is estimated as the value of the metabolic rate of the person before entering the target space. The input parameters further include temperature, humidity, wind speed and radiant heat around the person,
The first estimation unit is
In the input process, further, the humidity, the wind speed, and the value of radiant heat determined according to the environment outside the target space, and the value of the temperature corresponding to each of the plurality of virtual patterns are input to the human body heat model. ,
The information processing apparatus according to claim 2, wherein a temperature value corresponding to the extracted virtual pattern is estimated as a temperature value around the person before entering the target space. The second value of the input parameter is a value of the weight, body surface area, and clothing amount of the person estimated by the first estimating unit, and a value of a metabolic amount determined from physical activity assumed in the target space. The information processing apparatus according to claim 2, further comprising: The second value of the input parameter is a value of the weight, body surface area, and clothing amount of the person estimated by the first estimating unit, and a value of a metabolic amount determined from physical activity assumed in the target space. The information processing apparatus according to claim 4, further comprising: a temperature, a humidity, a wind speed, and a value of radiant heat that are determined according to the environment in the target space. The acquisition unit is
Receive the output value of the distance measuring sensor for measuring the distance to the person,
The information processing apparatus according to claim 1, wherein a heat distribution measured by the heat distribution measuring device is acquired as the target heat distribution when the output value reaches a predetermined distance. .. The information processing apparatus according to any one of claims 1 to 7, further comprising an air conditioning control unit for controlling the air conditioning apparatus based on the support information. A step of acquiring a target heat distribution generated by the heat distribution measuring device measuring a person entering the target space;
By using the human body heat model which outputs the value of the output parameter relating to the heat of the human body in response to the value of the input parameter affecting the heat of the human body and the target heat distribution, the person before the person enters the target space Estimating a first value of the input parameter,
Estimating a thermal sensation of the person after entering the target space using the human body heat model;
A step of accumulating the thermal sensation estimated each time the target heat distribution is acquired in a predetermined period, and generating a distribution of people for each thermal sensation from the accumulation result;
A step of generating support information for supporting control of the air conditioner based on the distribution of the number of people,
The step of estimating the thermal sensation of the person includes
With the first value of the output parameter obtained by inputting the first value of the input parameter to the human body heat model as an initial value, the second value of the input parameter after entering the target space is set to the human body. Calculating a second value of the output parameter by inputting into a thermal model;
Estimating the thermal sensation of the person based on the second value of the output parameter. A step of acquiring a target heat distribution generated by the heat distribution measuring device measuring a person entering the target space;
By using the human body heat model which outputs the value of the output parameter relating to the heat of the human body in response to the value of the input parameter affecting the heat of the human body and the target heat distribution, the person before the person enters the target space Estimating a first value of the input parameter,
Estimating a thermal sensation of the person after entering the target space using the human body heat model;
A step of accumulating the thermal sensation estimated each time the target heat distribution is acquired in a predetermined period, and generating a distribution of people for each thermal sensation from the accumulation result;
A step of generating support information for supporting control of the air conditioner based on the distribution of the number of people,
The step of estimating the thermal sensation of the person includes
With the first value of the output parameter obtained by inputting the first value of the input parameter to the human body heat model as an initial value, the second value of the input parameter after entering the target space is set to the human body. Calculating a second value of the output parameter by inputting into a thermal model;
Estimating the thermal sensation of the person based on the second value of the output parameter.

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