JP3465849B2 - Room air conditioner - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a room air conditioner .

[0002]

2. Description of the Related Art A device for controlling a physical quantity as a stimulus at the time of occurrence to bring a pleasant sensation to a person is widely used as a home electric appliance. For example, there are electric carpets for warming, fans for obtaining cool breeze, and air conditioners for heating and cooling, which give people comfort, exhilaration, and comfort.

The amount of irritation they give to a person is generally constant. For example, an electric fan sends a fixed amount of wind having a set strength. The user initially feels refreshed by this wind,
This feeling of extinction gradually fades due to the acclimation phenomenon. And it can even be uncomfortable. This phenomenon is also found in various other stimuli.

On the other hand, the above phenomenon is not observed with respect to natural wind. It is said that the natural wind feels comfortable because the wind speed and direction are constantly changing. In other words, it is said that the physical quantity as stimulation is fluctuating.

When the power spectrum of the wind speed fluctuation of natural wind is observed, 1 / f fluctuation in which the power is proportional to the reciprocal of the frequency is seen as shown in FIG. FIG. 47 is a power spectrum diagram of the wind speed fluctuation in which the horizontal axis represents the frequency of the wind speed fluctuation and the vertical axis represents the power of the wind speed. This 1 /
f Fluctuations are found not only in the wind but also in many natural phenomena such as river murmuring and sound of waves. It is also a characteristic common to nature and creatures, which is also seen in human heartbeat fluctuations, vocal pitch fluctuations, and birdsongs.

Products have been developed which take in these fluctuations to prevent the acclimatization phenomenon and give a refreshing feeling.
For example, Matsushita Technical Report (Takeda et al. “Pursuit of 1 / f fluctuation comfort in fans” Matsushita Technical Report Vol. 35, No. 6, November 1989)
There is a fan introduced in.

As a technique for giving this fluctuation, there is one disclosed in Japanese Patent Publication No. 61-56805.
Here, there are shown an operating means for operating a physical quantity and a means for generating an irregular signal having a different power spectrum and controlling the operating means. As a concrete example, an electric stove is used as an example to switch between two heaters having large and small heat values by the irregular signal.

The generation of these irregular signals is constituted by a read-only memory storing a plurality of irregular data series, a counter for addressing the read-only memory, and a switch for selecting one of the irregular data. With this configuration, it is possible to give various fluctuations to the heat generation amount, alleviate the habituation phenomenon of the user, and give a comfortable feeling.

[0009]

However, the above-mentioned prior art has the following problems. Above Japanese Patent Publication Sho 61-5
The conventional technique described in Japanese Patent No. 6805 has an irregular signal generating means for controlling an operating means for operating a physical quantity and giving fluctuation to the physical quantity. The generating means has a read-only memory that stores a plurality of irregular data series, and previously stores time series data in which the power spectrum is inversely proportional to the frequency f.
For example, time series data of 1 / f and 1 / f ² are stored. The storage capacity for this is roughly calculated as follows.

Now, considering the case of wind, the range of the frequency of wind speed fluctuation is set to an upper limit of about 1 Hz and a lower limit of 0.0 as shown in FIG.
Up to 1 Hz. In order to reproduce the fluctuation up to the upper limit frequency, it is necessary from the sampling theorem to store the time series data with such fineness as to have at least twice this frequency component. That is, the minimum time unit of the data series is 500
ms.

Further, the maximum time length of the data series is from the viewpoint of the lower limit frequency, that is, the gentlest fluctuation frequency, and since this data series is repeatedly used, this repetition cannot be detected by humans. It is decided from the viewpoint of time, that is, time that does not give a feeling of repetition to a person's memory. Usually this time is several hundred seconds.
The lower limit of the human sense detection frequency is almost the same as this time.

If this time is set to 200 seconds, the number of data series, that is, the storage capacity can be determined. 200 in the above example
/0.5=400 data are required. If the number of bits of 1 data is 8 bits, a large storage capacity of 3.2 kilobytes is required. If there are multiple data sequences, the storage capacity will increase by 3.2 kilobytes.

Further, since the time series data is fixed, the number of fluctuations that can be given is limited to the number of types of data series, and the range of selection by the user is narrow. For example, changing the shape of fluctuation every 365 days requires enormous storage capacity.

Further, there is no mention of changing the type (pattern) of fluctuation depending on the user's health condition, activity condition, and psychological condition. A person's feeling of comfort changes depending on the person's condition. Therefore, it should be more comfortable to change the type of fluctuation (pattern) depending on the person's condition.

Further, there is no mention of changing the type (pattern) of fluctuation depending on the season, time, and weather. For example, considering the natural breeze, it changes with the season, time, and weather. Therefore, in order to give a person a natural feeling of comfort, it is preferable to change the type (pattern) of fluctuations according to the season, time, weather and the like.

Furthermore, even if the irregular data series has a power spectrum of 1 / f, the stimulus physical quantity given to a person may not have a power spectrum of 1 / f depending on the characteristics of the physical quantity operating means. The above conventional technique can be regarded as a simple system shown in FIG.

FIG. 48 is a block diagram showing a simplified model of a physical quantity control device according to the prior art. In the figure,
It is a system in which the irregular data series a is an input and is input to the physical quantity control means b in the physical quantity operation means E to drive-control an actuator c to which electric energy is supplied and to give a physical stimulus d to a person. Will be recognized.

Here, if the physical quantity control means b, the actuator c, and the spatial transfer path from the actuator c to the person are regarded as one transfer function, then they are simplified as shown in FIG. In this system, it is apparent that the frequency characteristic of the physical stimulus d to the human is a product of the frequency characteristic of the input irregular data sequence a and the frequency characteristic of the transfer function. In other words, the power spectrum of the irregular data series is modified and becomes a physical stimulus to humans.

The above-mentioned conventional example only mentions that the irregular data sequence has a power spectrum of 1 / f, and for the above-mentioned reason, the user is given a physical stimulus having an intended power spectrum. May not be possible. Moreover, the method of changing the slope of the power spectrum of the physical stimulus is only mentioned by changing the type of the irregular data series stored in advance, and for this purpose, a huge storage capacity is required as described above. Becomes

Further, there is no mention of changing the slope of the power spectrum of the physical stimulus according to the user's health condition, activity condition, or psychological condition. A person's feeling of comfort changes depending on the person's condition. Therefore, it should be more comfortable to change the slope of the power spectrum of the physical stimulus in the human state.

Further, there is no mention of changing the slope of the power spectrum of the physical stimulus depending on the season, time and weather. For example, considering the natural breeze, it changes with the season, time, and weather. Therefore, in order to give a person a natural feeling of comfort, it is preferable to change the slope of the power spectrum of the physical stimulus depending on the season, time, weather and the like.

An object of the present invention is to solve the above problems and to provide a user with a large number of varying (having fluctuating) physical stimuli even if the memory capacity is small so that the user can maintain a comfortable feeling. To provide a room air conditioner .

Further, the room air conditioner capable of changing the previous fluctuation pattern depending on the physiological condition, the psychological condition of the user, the season, the time zone, the weather, or the like to give a physical stimulus to always obtain an optimal comfort is provided. To do.

[0024]

To achieve the above object, the room air conditioner according to the present invention stores several kinds of numerical values.
At least one of the storage means and the numerical value is an initial value or
The equation set as a coefficient value is repeatedly calculated and 2 systems
Calculating means for creating time series data of
Converter for converting the drive signal into a drive signal, and the drive signal
Output to the drive circuit of the fan motor or compressor motor.
Drive to control the rotation speed of fan motor or compressor motor
A signal generator, of the two time series data,
Time-series data is converted as a drive signal for the fan motor
The number of times of thinning out is less than the number of times
The time series data is converted as the drive signal of.

[0025]

[0026]

[0027] further comprising a calendar means for outputting a date time as data, Yi the output of the calendar means
By controlling the degree of operation by changing the degree of operation depending on the season or the date and time, it is possible to give the user a physical stimulus that fluctuates with the season or day even if the memory capacity is small.

Furthermore the outdoor temperature, humidity, atmospheric pressure, with outside air condition detecting means for detecting the like airflow, and control the degree of operation in dependence on the detection output of the ambient air condition detecting means, changes the degree of operation depending on the weather By doing so, it is possible to give the user a physical stimulus that fluctuates depending on the weather even if the memory capacity is small.

Further, an activity amount detecting means for detecting an activity amount which is a movement of a person is provided, and the detection output of the activity amount detecting means is provided.
Dependent by controlling the degree of operation, enough of the operation by the amount of activity
By changing the degree , it is possible to give the user a physical stimulus with fluctuations that changes according to the movement of the person even if the memory capacity is small.

Further, a physiological condition detecting means for detecting the physiological condition of a person from blood pressure, heart rate, body temperature, etc. is provided, and the degree of operation is controlled depending on the detection output of the physiological condition detecting means,
By changing the degree of operation according to the physiological condition of the user, it is possible to give the user physical fluctuations that fluctuate according to the health condition of the user even if the memory capacity is small.

Further, a psychological state detecting means for detecting the psychological state of a person from the electroencephalogram, the heart rate, the amount of sweat, etc. is provided, and the degree of operation is controlled depending on the detection output of the psychological state detecting means. By changing the degree of operation depending on the state, it is possible to give the user a physical stimulus that fluctuates according to the user's psychological state even if the memory capacity is small.

Further, a frequency response correcting means having a transfer characteristic opposite to a transfer function representing the frequency response characteristic of the operating means is provided, and the drive power signal is corrected by the frequency response correcting means and added to thereby correctly reproduce the power spectrum of the operation. It has become possible to give the user physical stimulation.

[0033]

[0034]

According to the present invention, in addition to the fluctuation of the wind, the set temperature in the room
Can provide a room air conditioner with different fluctuations
It

[0035]

[0036]

[0037]

[0038]

[0039]

The calendar means is composed of a known calendar IC for outputting data such as date, time, etc.,
Use this data. Examining the time-series pattern obtained by iterative calculation of the equation, a plurality of typical ones,
The obtained initial values or coefficient values are stored in the storage means. Depending on the output of the calendar means, at least one initial value or coefficient value for outputting the fluctuation time series suitable for the season or the time zone is selected from the storage means, and this is used to repeatedly calculate the above equation. The operation means is driven and controlled by the irregular data signal obtained at this time.

In this way, it becomes possible to give the user a physical stimulus having an optimum fluctuation pattern according to the season or the time zone. Further, a counter means is provided. And using the date and time data which is one of the output data of the calendar means, the counter means is counted up every day at midnight, and the time series of the solution of the above equation is sensitive to the initial value or the coefficient value. The counter means sets an initial value or a coefficient value. This makes it possible to give the user different physical stimuli with different fluctuation patterns every day.

The outside air condition detecting means is composed of a temperature sensor, a humidity sensor, an atmospheric pressure sensor, a wind speed sensor, etc., detects the outdoor temperature, humidity, atmospheric pressure, air flow, etc., and outputs the data. A time series pattern obtained by repeating the above equations is examined in advance, and a plurality of typical ones are stored in the storage means as initial values or coefficient values obtained from them. For example, a small step reminiscent of an autumn breeze in hot summer weather, that is, a time series pattern with a small fluctuation width and a short change time, a spring breeze associated with a cold breeze in winter weather, that is, a small fluctuation width and a change time An initial value or a coefficient value that produces a long time series pattern is stored.

At least one initial value or coefficient value that outputs irregular time-series data suitable for the weather is selected by the output of the outside air condition detecting means, and this is used to repeatedly calculate the above equation. The operation means is driven and controlled by the irregular data signal obtained at this time. In this way, it becomes possible to give the user a physical stimulus having an optimal fluctuation pattern according to the weather.

The activity amount detecting means is composed of a pyroelectric sensor (infrared detecting means for detecting infrared rays emitted from a person as an indicator of the amount of person's activity) and the like. The amount of activity that is the amount of is detected and this data is used. A time series pattern obtained by repeating the above equations is examined in advance, and a plurality of typical ones are stored in the storage means as initial values or coefficient values obtained from them.

For example, when the amount of activity is large, the change is drastic,
In other words, the initial value or coefficient value that produces a time-series pattern with a large fluctuation width and a short change time, or a change with a small amount of activity, that is, a time series pattern with a small fluctuation width and a long change time, is stored. Keep it. At least one initial value or coefficient value that outputs irregular time-series data suitable for a person's activity is selected by the output of the activity amount detecting means, and this is used to repeatedly calculate the above equation. The operation means is driven and controlled by the irregular data signal obtained at this time. In this way, it becomes possible to give the user a physical stimulus having an optimal fluctuation pattern according to the activity of the person.

The physiological condition detecting means detects the physiological condition of the user from blood pressure, heart rate, body temperature, etc., and outputs this data. A time series pattern obtained by repeating the above equations is examined in advance, and a plurality of typical ones are stored in the storage means as initial values or coefficient values obtained from them. For example, an initial value or coefficient value that produces a time-series pattern having a normal fluctuation when healthy and a time-series pattern that has a gentle change when sick, that is, has a small fluctuation width and a long change time is stored.

At least one initial value or coefficient value that outputs irregular time-series data suitable for a person's health condition is selected from the output of the physiological condition detecting means, and the above equation is repeatedly calculated using this value. The operation means is driven and controlled by the irregular data signal obtained at this time. In this way, it becomes possible to give the user a physical stimulus having an optimal fluctuation pattern according to a person's health condition.

The psychological state detecting means detects the psychological state of the user from the electroencephalogram, heart rate, sweat rate, etc., and outputs this data. A time series pattern obtained by repeating the above equations is examined in advance, and a plurality of typical ones are stored in the storage means as initial values or coefficient values obtained from them. For example, when you are calmly relaxing, a time series pattern with ordinary fluctuations is stored, and when you are frustrated, the change is gentle, that is, the initial value or coefficient value that produces a time series pattern with a small fluctuation width and a long change time is stored. I'll do it.

At least one initial value or coefficient value that outputs irregular time-series data suitable for the psychological state of a person is selected by the output of the psychological state detecting means, and this is used to repeat the above equations. The operation means is driven and controlled by the irregular signal obtained at this time. In this way, it becomes possible to give the user a physical stimulus having an optimal fluctuation pattern according to a person's psychological state.

The frequency response correction means is composed of a digital filter or the like, and an electric signal based on the time series data of the solution of the above equation is input to this. When the operating means has a certain frequency response characteristic, the frequency response characteristic opposite to this characteristic is also given to the frequency response correcting means so that the frequency response characteristic of the frequency response correcting means and the operating means is flat, and the operating means The user is given a physical stimulus that correctly reproduces the power spectrum of the drive signal given to the user.
In this way, it becomes possible to match the signal spectrum shape given to the operating means with the spectrum shape of the physical stimulus given to the user.

[0051]

[0052]

[0053]

[Examples] Natural phenomena, social phenomena, biological phenomena, etc. often appear to be irregular, complicated, or unstable. For example, even in the form of objects, the shapes of clouds and coastlines are so complicated that they are far from conventional geometric figures. River flow,
The atmospheric flow that is the source of the weather forecast is too complicated to predict. Stock price fluctuations due to human economic activity are also the same.
At first glance, even if it shows a stable change according to a certain law, there are many phenomena in which it changes in a complicated way when it is observed for a long time and compressed. The above stock price fluctuation is a good example of this.

In recent years, in the conventional mathematics or physics, the phenomenon described above, which has been ignored and avoided as noise, is exposed. One is the fractal geometry proposed by Mandelbrot as a step to grasp these phenomena geometrically, and Lorenz (Lorenz).
z) The various chaotic phenomena mentioned above, which originated from the strange trajectory (strange attractor) found in the behavior of the solution of the three-variable nonlinear equation, which is a simple atmospheric circulation model, are grasped as nonlinear phenomena under certain conditions. This is the so-called chaos theory.

It can be said that the present invention is based on the idea of the above-mentioned fractal geometry and chaos theory and applies this engineeringly. According to fractal geometry, complex shapes in nature can be expressed by repeating simple rules. As a specific example, a graph shown in FIG. 49A using a recursive function (reduction map) as an expression of tree branching is known. Note that FIG. 49 is an explanatory diagram showing a tree by a recursive function.

The graph shown in (a) of FIG. 49 shows that "a certain straight line from the tip of a certain straight line is multiplied by a reduction rate 2
It is obtained by repeating the rule "draw a straight line in a book". If the angle value and the reduction rate are modulated with random numbers or the like, a more natural tree shape (shown in (b) of FIG. 49) can be obtained.

As an example of the chaos theory, in addition to Lorentz, the logistic equation as a model for increasing / decreasing the number of individuals is known. This equation has been confirmed to be effective in various experiments as a model of organism growth. Mei corrects this equation by Euler's difference method into the difference equation (also called logistic map) shown in the following equation (1) and conducts a numerical experiment to show that a chaotic phenomenon appears depending on conditions.

[0058]

[Equation 1]

In the above equation (1), A is a given coefficient, and if the initial value X ₀ is set as the value of X _n , and is calculated, X
The value of _{0 + 1} is obtained. If the value of X _{0 + 1} is further calculated as the value of X _n , the value of X _{n + 1} can be obtained. By repeating this, time series data is obtained.

In FIG. 50, when the initial value X ₀ = 0.3 is set and n = 100 to 200 is repeated by using the equation (1), the finally obtained value of X _n is calculated by the coefficient A. Is a graph plotted while changing as a parameter,
This is called the bifurcation diagram of the logistic difference equation.

From FIG. 50, when the coefficient A is less than 3.0, the value of X _n finally obtained converges to a certain constant value when repeated up to about n = 100 to 200. When A exceeds 3.0 and is less than 3.57 (the coefficient A of 3.57 is expressed as Ac in the sense of a critical value), a periodic function of 2 cycles, 4 cycles, ... 2n cycles appears. It can be seen that when the coefficient A exceeds Ac (that is, 3.57), a chaotic phenomenon occurs in which the value of X _n is irregularly distributed in a certain range.

In FIG. 51A, A = 3.7 and X ₀ =
The chaos phenomenon (time series of the value of _Xn ) in the case of 0.3 is shown. In FIG. 7B, A = 3.7001, X ₀ = 0.3.
In the case of, A = 3.7 and X ₀ = 0.300 in FIG.
In the case of 1, each chaos phenomenon (time series of X _n values)
Indicates. FIG. 51 is a graph showing time series data of the value of X _n as a solution obtained by solving the logistic difference equation.

As described above, the chaotic phenomenon can be created from a simple rule such as the above equation (1), which is sensitive to the initial value and the coefficient value, and the time series data created by only slightly different them. Has a completely different pattern.

The above fractal geometry and chaos theory have the following properties. (1) A simple natural phenomenon can be expressed by repeating simple rules. (2) The non-linear equation causes a chaotic phenomenon under certain conditions. (3) The time series data generated in this case is sensitive to the initial value. (4) Similarly, it is sensitive to the coefficient value. (5) Further, the spectrum of this time series data signal is continuous.

The present invention is to provide a time-series data signal generator having the above properties, and control the actuator by the time-series data signal from the signal generator to give a comfortable physical stimulus to a person. . First, a reference example will be described.

FIG. 1 is a block diagram showing an indoor unit of a room air conditioner (hereinafter referred to as an air conditioner) which is a reference example of the present invention . In FIG. 1 , 1 is an air conditioner indoor unit, 2 is a fan motor, 3 is a fan motor drive circuit, 4 is a fan motor drive signal generator, and 5 is a control circuit for controlling the entire indoor unit.

The fan motor 2 is driven by the fan motor drive circuit 3 to rotate. Fan motor drive circuit 3
Applies a constant voltage to rotate the fan motor 2 at a constant rotation speed. Then, a constant wind velocity is sent to the user.

FIG. 2 is a block diagram showing the configuration of the fan motor drive signal generator 4 shown in FIG. 2, the fan motor drive signal generator 4 includes a microprocessor (hereinafter referred to as MPU) 41, a read only memory (hereinafter referred to as ROM) 42, a random access memory (hereinafter referred to as RAM) 43, and a DA converter 44. Composed. The ROM 42 stores a program and necessary constant data. The RAM 43 is used for temporary storage of variables and the like necessary during the calculation.

The case of generating a drive signal by using the solution of the nonlinear difference equation shown in (Equation 1) will be described. A program for calculating (Equation 1) is stored in the ROM 42 in advance. FIG. 3 is a schematic flowchart showing a program for this calculation.

As shown in FIG. 3, first, the coefficient A and the initial value X ₀ are set, and according to (Equation 1), X ₁ = AX
Calculate ₀ (1-X ₀ ). Then, this value is output as an analog value by the DA converter 44. Next, the value of X ₁ is substituted into (Equation 1), X ₂ is calculated, converted into an analog value by the DA converter 44, and output. Hereinafter, by repeating this, the electric signal based on the time series data shown in FIG. 51 can be obtained.

In FIG. 3, the time required for the iterative calculation process of (Equation 1) is equal to the hold time of the drive signal output from the DA converter 44. The time required for the calculation of (Equation 1) and the hold time of the drive signal may be different, and this can be easily executed by a program. FIG. 4 is a schematic flowchart showing the program in this case.

In FIG. 4, by performing DA conversion output only once out of m times of repetition, the calculation processing time of m times is one hold time. This corresponds to the process of thinning the output time series of (Equation 1) to 1 / m. Also (Equation 1
It is obvious that the hold time can be freely changed within a range of one arithmetic processing time or longer by inserting other necessary processing or a time delay processing before and after the arithmetic processing of the expression).

In many cases, it is necessary to adjust the time between the time-series data obtained directly from (Equation 1) and the drive (DA conversion) signal actually used. For example, in FIG. 1, if the response time of the fan motor drive circuit 3 is 100 mS and the calculation time of (Equation 1) is 1 mS, then 99 m
It is necessary to allocate the time of S to other necessary processing or time delay processing.

In the above time adjustment, in FIG. 2, a sample hold circuit (not shown) is inserted after the DA converter 44, and the DA converter output of the flowchart of FIG. Obviously, it is also possible to sample and hold with a pulse of an oscillator (not shown).

When the numerical value between X _n and X _{n + 1} which is the output of (Equation 1) is DA converted into a fan motor drive signal, the numerical values of X _n and X _{n + 1} are linearly interpolated. Alternatively, spline interpolation may be performed and output. FIG. 5 is a flowchart showing this linear interpolation method. Interpolation data X _DA sequentially D
Convert A.

Next, referring to FIGS. 1 and 2, the DA converter 4
A process for giving the drive signal obtained in 4 to the fan motor drive circuit 3 will be described. FIG. 6 is a graph showing the relationship between the voltage (input voltage) applied to the fan motor drive circuit 3 and the rotation speed of the fan motor 2.

As shown in FIG. 6, when a certain voltage (input voltage) is applied, it is possible to obtain a rotation speed approximately proportional to the voltage. The wind speed obtained is proportional to this rotation speed. Now, the numerical value obtained by successively calculating (Equation 1) is
As shown in FIG. 50, the value is between 0 and 1. D
The A converter converts this numerical value into the voltage required for rotation shown in FIG.

At this time, depending on the specifications of the DA converter 44, it is necessary to convert the calculated numerical values in accordance with the specifications. For example, NEC 8-bit DA converter (μPD7001)
Converts the digital numerical data represented by 8 bits of 0 to 255 from 0V to a predetermined positive voltage (for example, 4V) set externally. Therefore, the previous calculation result of 0
The required voltage of 0 to 4V can be obtained only by converting the numerical value in the range of 1 to 1 into the numerical value of 0 to 255.

FIG. 7 is a flow chart showing a program including this numerical value conversion processing. The result of multiplying X _n by a predetermined constant is DA-converted. As shown in FIG. 50, the range in which the X _n value can be taken differs depending on the coefficient A. Therefore, the above constant must be determined according to the coefficient A.

In this way, the irregular fluctuation (fluctuation), which is the calculation result of (Equation 1) for the first time, is correctly converted into the driving voltage, the rotation speed of the fan motor 2 is changed, and the strength of the wind as a stimulus given to a person. You can give a feeling of comfort by giving fluctuations to (wind speed).

It is considered that the center level of the wind speed is set to a predetermined value and the fluctuation (fluctuation) range at that time is set to a certain width. In FIG. 6 showing the relationship between the drive voltage and the rotation speed, what is indicated by (a) is the central rotation speed, and it is considered that the center rotation speed is changed within the rotation speed range indicated by (b). Now, it is considered that the DA converter 44 of FIG. 2 outputs a voltage of 0 to 4 V by inputting 8-bit data of 0 to 255. From FIG. 6, if an input voltage of 1.5 to 3 V is applied to the fan motor drive circuit 3, a fan motor rotation speed of 800 to 1600 rpm can be obtained. For this purpose, the calculation result of (Equation 1) X _n is 1.5 to 3
It is necessary to convert the voltage to V.

FIG. 8 is a characteristic diagram for explaining such a linear conversion process. In FIG. 8, the value of X _n is converted into X _DA using the proportional (slope a, intercept b) relation, and this value is DA converted to obtain the required input voltage. Figure 9
3 is a flowchart showing a program including such linear conversion processing.

The parameters for this linear conversion (the slope a and the intercept b of the conversion line in FIG. 8) are set at the start of the flowchart in FIG. 9, and a plurality of parameters are stored in advance in the ROM 42 (FIG. 2). The program may be started by storing a set of parameters and selecting a set of parameters with a signal from the control circuit 5 (FIG. 1) to the fan motor drive signal generator 4.

Such processing can be performed by providing a circuit for converting the voltage between the DA converter 44 and the fan motor drive circuit 3, but it is complicated to freely convert the center voltage and the amplitude at the same time. Requires electronic circuitry. As in this example , M
The PU41 program is superior in cost and flexibility (flexibility).

FIG. 10 is a graph showing another example of the relationship between the voltage (input voltage) applied to the fan motor drive circuit 3 and the rotation speed of the fan motor 2. The relationship between the input voltage of the fan motor drive circuit 3 and the rotation speed of the fan motor 2 rarely has the linear proportional relationship shown in FIG. Figure 1
As shown in 0, there is a case where there is a non-linear relationship in which a dead zone in which the input voltage is not applied and rotation speed is proportional to the square of the input voltage. Therefore, in order to reflect the time series data signal obtained by (Equation 1) in the rotation speed without distortion, it is necessary to correct this non-linearity.

FIG. 12 is a flow chart showing a program including the non-linearity correction conversion processing. The dead zone can be avoided by adding a constant offset value to the calculation result of (Equation 1). For the correction of the non-linearity, an inverse function that has an inverse relationship with the above-mentioned non-linearity is prepared, and
The calculation result of (expression) is corrected by this inverse function and output.

FIG. 11 is a characteristic diagram for explaining an example of this non-linearity correction processing. Here, X _n is 0.
There is a value in the range of 0 to 1.0, and the drive circuit input voltage and the fan motor rotation speed are in the relationship shown in FIG. 10. At this time, the rotation speed of the fan motor 2 is 0 to 1600 rpm which is proportional to the value of X _n. Think about getting.

First, X _n is converted in a proportional relationship so that a voltage of 1 to 3 V can be obtained as the output of the DA converter (see FIG. 1).
1 (a), which is the same as the linear conversion processing in FIG. 10). Next, this is distorted by the inverse nonlinear function f ((b) of FIG. 11). Then, this is DA converted and input to the fan motor drive circuit 3 ((c) of FIG. 11).

In the explanation of FIG. 11, for easy understanding, all the input / output scales for conversion are shown as the output of the DA converter, that is, the input voltage of the fan motor drive circuit. From (a) and (c) of FIG. 11, it is possible to establish a proportional relationship between X _n and the rotation speed by interposing an inverse nonlinear function f. If the relationship between the drive input voltage and the rotation speed is simple nonlinear, the inverse nonlinear function f can be easily obtained analytically. For example, if the former is Y = X ² , then the latter is Y = X.

However, it is generally complicated as shown in FIG. In this case, it is preferable to numerically obtain from the polygonal line approximation and give the inverse nonlinear function f as table data. When the calculation processing capacity is low like the MPU, it is not desirable to perform the non-linearity correction by the inverse function calculation because it takes too much processing time. In this case, the ROM 42 is previously
Store the above inverse function as table data in
It is desirable to correct the calculation result of (Equation 1) by table lookup and output.

Such processing can be performed by providing a circuit for converting a voltage between the DA converter 44 and the fan motor drive circuit 3, but a non-linear conversion of the voltage requires a complicated electronic circuit. . It is more cost effective to carry out the program of the MPU 41 as in this embodiment.

By the above processing, finally, the calculation result of (Equation 1) can be correctly reflected in the rotation speed. In this way, the irregular fluctuation (fluctuation) that is the calculation result of (Equation 1) can be converted into the fan motor drive voltage that correctly reflects the fluctuation, and the rotation speed of the fan motor 2 can be changed, so that the wind strength as a stimulus given to a person can be increased. It gives a feeling of comfort by giving fluctuations to the wind speed.

The time adjustment function, the numerical conversion processing, the linear conversion processing, and the non-linearity correction conversion processing have been performed in separate flow charts to simplify the explanation of the operation, but one flow chart may include all the processings. Is clear.

FIG. 13 shows fractal geometry (reduction map).
FIG. 9 is a block diagram showing another example of a fan motor drive signal generator in the case of creating a complicated signal using random number data based on the above idea. 13, the same symbols as those in FIG. 2 indicate the same things. 48 is 0 to 1
Is a random number generation circuit that generates uniform random number data up to.

The following (Equation 2) is a mapping that creates a random point distribution called Levi dust. This was devised as a model of the distribution of stars in the universe.
A complex distribution can be given by repeating mapping while giving the amplitude and angular frequency of a simple sine wave function by random numbers (plotted on a plane with X _n and Y _n as coordinate points). The variable D of the following (Equation 2) is a fractal dimension, and changing this changes the distribution.

[0096]

[Equation 2]

Similar to (Equation 1), it is possible to create a complicated time series data signal by repeating the calculation. FIG. 14 shows the output time series data X _n of the above (Equation 2).
It is a graph which shows an example.

FIG. 15 is a flow chart showing a program for generating a fan motor drive signal using (Equation 2). The fractal dimension D and the initial value X ₀ are set, while the random number value RND is read from the random number generation circuit 48 (FIG. 13), the above (Equation 2) is repeatedly calculated and DA-converted to obtain the time series data shown in FIG. Get the signal.

By changing the fractal dimension D as a coefficient, the state of the time series pattern can be greatly changed.
By inputting this to the fan motor drive circuit 3, the rotational speed of the fan motor 2 is changed, fluctuations in the wind strength (wind speed) as a stimulus to a person are given, and a feeling of comfort can be evoked. . It is obvious that the same operation as the random number generation circuit 48 can be performed by a program, and the random number generation circuit 48 can be omitted.

The following (Equation 3) is a map called a sine circle map derived by the mathematician Arnold. In this mapping, when the coefficients Ω and K are set to predetermined values, the intermittent time-series data signal as shown in FIG. 16 is greatly different from the continuous time-series signal (FIGS. 51 and 14) obtained in the previous mapping. Is obtained.

That is, FIG. 16 is a graph showing an intermittent time series data signal obtained by calculating (Equation 3). FIG. 17 is a flow chart showing a program for generating a fan motor drive signal using this mapping. By generating a fan motor drive signal in this way, wind can be generated intermittently and the wind can be fluctuated.

[0102]

[Equation 3]

According to the reference example of FIG. 1 described above, complicated time series data signals can be created by a simple program without requiring a large storage capacity, and the number of revolutions of the fan motor is controlled by this, so that the user can It can provide comfortable wind with fluctuation.

FIG. 18 is a block diagram showing a reference example of the present invention. In the figure, the same symbols as in FIG. 1 indicate the same items. In FIG. 18, 6 is a push button switch, and 7 is a switch selection information circuit that detects which push button switch is pressed and sends this push button switch information to the control circuit 5.

[0105] According to the present example, the user preferences, the reference in FIG. 1
The center wind speed of the fluctuation wind, the width of the fluctuation, or the fluctuation pattern of the wind described in the example can be selected. For example, three push button switches, strong, medium, and weak, are prepared. When the user presses the strong button, the switch selection information circuit 7 transmits this information to the control circuit 5.

Then, the control circuit 5 suspends the current fan motor drive signal generation by the program of the fan motor drive signal generator 4 and temporarily stores a plurality of parameters for linear conversion (conversion straight line) stored in the ROM 42. The parameters of the central wind speed and the fluctuation width corresponding to the strong wind are selected from the slope a and the intercept b), and the program is instructed to start again. As a result, the wind caused by the rotation of the fan motor 2 changes from the current state to the wind state corresponding to the strong wind. By such an operation, the user can obtain a fluctuating wind with a desired wind speed.

In order to change the output time-series data by (Equation 1), the initial value or the coefficient value may be slightly shifted as shown in FIG. For example, if several kinds of values are stored in the ROM 42 as initial values, one of them is read out, and calculation is performed according to the flowchart of FIG. 3, completely different time series data can be obtained.
The same applies when several types of coefficient values are prepared.

As is apparent from the above description of the operation, if the push button switch 6 can be used to select one of the initial value and the coefficient value, the wind fluctuation pattern can be selected as desired by the user. Then, it becomes possible to select not only the central wind speed and the fluctuation width, but also the fluctuation pattern.

As can be seen by comparing FIG. 51, which is the output time series data of the above (Equation 1), with FIG. 16, which is one of the output time series of the above (Equation 3), an irregular time series is shown. The pattern of time-series data varies greatly depending on the type of nonlinear difference equation used to create a data signal. The nonlinear difference equation itself is simple, and the program capacity for executing it is small. Therefore, as shown in FIG. 19, if several kinds of non-linear difference equations are programmed in advance and one of them is selected by the push button switch 6, the user can change the irregular time series data signal You will be able to select the fluctuation state of the wind.

FIG. 19 is a flow chart showing a program in which several types of non-linear difference equations are programmed in advance and one of them is selected to generate an irregular time series data signal.

As described above, according to this example , a complicated program can be used to create a complicated time series data signal without requiring a large storage capacity, and the number of revolutions of the fan motor can be controlled by this. Can provide some comfortable breeze.
Further, the user can freely select the central air volume, the fluctuation width or the fluctuation pattern according to his or her preference.

FIG. 20 is a block diagram showing a reference example of the present invention. In the figure, the same symbols as in FIG. 1 indicate the same items. Besides, in FIG. 20, reference numeral 8 is a calendar circuit for outputting date and time data. In this example , the central air volume, fluctuation width or fluctuation pattern described in the reference example of FIG. 18 is automatically selected depending on the date and time data output by the calendar circuit 8. Is.

The control circuit 5 judges the season from the date data output from the calendar circuit 8 and controls the program of the fan motor drive signal generator 4. Specifically, the fluctuation pattern suitable for the season is automatically selected by changing the set value of the coefficient A or the initial value X _{0 in} the flowchart of FIG. 3 when the season changes. As a set value, a time series pattern obtained by an equation is examined in advance, and a plurality of typical ones suitable for the season, for example, four are selected and stored in the ROM 42.

A counter (not shown) is provided in the fan motor drive signal generator 4, and the counter is incremented every midnight according to the time data output from the calendar circuit 8. By using this counter value as an initial value and generating a fan motor drive signal in accordance with the flowchart of FIG. 3, it is possible to obtain daily fluctuation patterns.

As described above, according to the present embodiment, it is possible to automatically select the central air volume, the fluctuation width or the fluctuation pattern suitable for the season and send the comfortable wind to the user. It is also possible to give the user a wind having a different fluctuation pattern every day.

FIG. 21 is a block diagram showing a reference example of the present invention. In the figure, the same symbols as in FIG. 1 indicate the same items. In FIG. 21, in addition, 9 is an air conditioner outdoor unit, 10 is an air conditioner outdoor unit, and the outdoor temperature and humidity obtained from a temperature, humidity, barometric pressure sensor, etc.
It is an outside air state detection circuit that transmits data such as atmospheric pressure and wind speed to the control circuit 5.

The reference example shown in FIG. 21 is weather data output from the outside air state detection circuit 10, and is one for automatically selecting the central air volume, fluctuation width, or fluctuation pattern described in the example of FIG. .

The control circuit 5 determines the weather from the data such as temperature, humidity, atmospheric pressure, and wind speed output from the outside air condition detection circuit 10, and controls the program of the fan motor drive signal generator 4. Specifically, the fluctuation pattern suitable for the weather is automatically selected by changing the set value of the coefficient A or the initial value X ₀ of the flowchart of FIG. 3 when the weather changes.
As a set value, a time series pattern obtained by an equation is examined in advance, and a plurality of typical ones according to the weather are selected and RO
It is stored in M42.

As described above, according to the present reference example, it is possible to automatically select the central air volume, the fluctuation width or the fluctuation pattern according to the weather and send the comfortable wind to the user.

FIG. 22 is a block diagram showing a reference example of the present invention. In the figure, the same symbols as in FIG. 1 indicate the same items. In FIG. 22, reference numeral 11 denotes a pyroelectric sensor (an infrared sensor that detects infrared rays generated as a result of the activity of the human body) or the like to detect the amount of activity that is the amount of movement of the person, and outputs it to the control circuit 5. It is an activity amount detection circuit. In this reference example , the activity amount data output from the activity amount detection circuit 11 is used to automatically select the central air volume, the fluctuation width, and the fluctuation pattern described in the reference example of FIG.

From the activity amount data output from the activity amount detection circuit 11, the control circuit 5 determines the degree of movement of the user, and controls the program of the fan motor drive signal generator 4.
Specifically, the fluctuation pattern suitable for the movement is automatically selected by changing the set value of the coefficient A or the initial value X _{0 in} the flowchart of FIG. 3 when the activity amount value changes. As a set value, a time series pattern obtained by an equation is examined in advance, and a plurality of typical ones suitable for movement are selected and stored in the ROM 42.

For example, when the amount of activity is large, there is a sharp change, that is, the center air volume, the set value of a pattern in which the fluctuation width is large and the change time is short. Design to automatically select a set value for a pattern with a small and long change time.

As described above , according to the present reference example , the central air volume, the fluctuation width or the fluctuation pattern suitable for the movement can be automatically selected, and a comfortable wind can be sent according to the movement of the user.

FIG. 23 is a block diagram showing a reference example of the present invention. In the figure, the same symbols as in FIG. 1 indicate the same items. In FIG. 23, a temperature sensor, a pressure sensor, and the like 12 detect the physiological condition of a person such as blood pressure, heart rate, and body temperature, and the detected data is sent to the data transmission circuit 13.
It is a physiological state detection circuit that outputs to the control circuit 5 via the data receiving circuit 14.

Since the physiological condition detecting circuit 12 is in contact with the user to measure the user's body temperature, heart rate, etc., it is desirable to take a form like a wristwatch and perform data transmission / reception wirelessly. In this reference example , the central air volume, the fluctuation width, and the fluctuation pattern described in the reference example of FIG. 18 are automatically selected according to the physiological condition of the user output by the physiological condition detection circuit 12.

The control circuit 5 determines the health level of the user from the physiological condition data output by the physiological condition detection circuit 12, and controls the program of the fan motor drive signal generator 4. Specifically, the coefficient A or the set value of the initial value X ₀ in the flowchart of FIG. 3 is changed when the degree of health of the user changes, so that a fluctuation pattern suitable for the health condition of the user is automatically selected. .

As a set value, a time series pattern obtained by an equation is examined in advance, and a plurality of typical ones suitable for a health condition are selected and stored in the ROM 42. For example, when the person is healthy, the normal central air volume, the set value of the fluctuation width or the fluctuation pattern, and when the disease is stimulating, that is, the central air volume,
Design to automatically select the set value of the pattern with small fluctuation width and long change time.

As described above , according to the present reference example , the central air volume, the fluctuation width or the fluctuation pattern suitable for the user's health condition is automatically selected to send a comfortable wind according to the user's health condition. You can

FIG. 24 is a block diagram showing a reference example of the present invention. In the figure, the same symbols as in FIG. 23 indicate the same things. In FIG. 24, in addition, reference numeral 15 denotes a pressure sensor, a resistance sensor, a minute voltage sensor or the like, which detects the user's brain waves, heart rate, sweat rate, etc., and outputs the psychological state of the person to the control circuit 5 from these data. It is a state detection circuit.

In this reference example , the user's psychological state data output from the psychological state detection circuit 15 is used to automatically select the central air volume, fluctuation width or fluctuation pattern described in the reference example of FIG. is there.

The control circuit 5 determines the psychological state of the user from the psychological state data output by the psychological state detection circuit 15, and controls the program of the fan motor drive signal generator 4.
Specifically, the fluctuation pattern suitable for the psychological state of the user is automatically selected by changing the set value of the coefficient A or the initial value X ₀ of the flowchart of FIG. 3 when the psychological state of the user changes.

As a set value, a time series pattern obtained by an equation is examined in advance, and a plurality of typical ones suitable for the psychological state are selected and stored in the ROM 42. For example, when you are calmly relaxing, the central air volume, fluctuation width or fluctuation pattern setting value is normal, and when you are frustrated, the stimulus is gentle to calm down, that is, the central air volume, fluctuation width is small and the change time is long. Design to select the setting value automatically.

As described above , according to the present reference example , the central wind volume, fluctuation width or fluctuation pattern suitable for the user's psychological state is automatically selected to send a comfortable wind in accordance with the user's psychological state. You can

Based on the above , FIG. 25 shows one embodiment of the present invention .
It is a block diagram which shows an example . 25, the same symbols as in FIG. 1 indicate the same things. In addition, in FIG. 25, 16 is a drive signal generator, 17 is a compressor drive circuit, 18 is a compressor, and 19 is a heat exchanger.

FIG. 26 is a block diagram showing a structural example of the drive signal generator 16 shown in FIG. 26, the same symbols as those in FIG. 2 indicate the same things. In addition, 45 is a DA converter. The DA converters 44 and 45 are connected to the two ports of the MPU 41.

This embodiment shown in FIGS. 25 and 26 is similar to that of FIG.
In addition to the fluctuations of the wind described in the reference example , the same fluctuations are given to the indoor set temperature.

The operation of the drive signal generator 16 will be described with reference to FIGS. 25 and 26. The following (Equation 4) is a second-order difference equation (also called an Enon map) derived by the mathematician Enon.

[0138]

[Equation 4]

(Equation 4) has the same coefficient A,
By setting B to a predetermined value and repeating calculation, two irregular time series data signals (X _n and Y _n ) can be obtained. FIG. 27 is a flowchart showing a drive signal generation program in this case. Coefficients A and B and initial values X ₀ ,
Y ₀ is set and (Equation 4) is repeatedly calculated. The calculated values X _n and Y _n are input to the DA converters 44 and 45, respectively, and the fan motor drive circuit 3 and the compressor drive circuit 1 are used as the fan motor drive signal and the compressor drive signal of analog voltage.
7 is output.

The compressor drive circuit 17 rotates the motor of the compressor 18 at a rotation speed proportional to the applied voltage. The refrigerant material (Freon) enclosed at a pressure proportional to this rotation speed is circulated in the heat exchanger 19. The amount of heat exchanged in the heat exchanger 19 is proportional to the amount of air passing through the heat exchanger and the previous pressure. The room air temperature can be lowered during cooling by guiding the room air to the heat exchanger 19 and blowing it back into the room by the fan motor 2. Therefore, if the voltage applied to the compressor drive circuit 17 has an irregular fluctuation, the temperature of the room fluctuates in proportion to this.

FIG. 28 is a flow chart showing another program for generating a drive signal. This is obtained by thinning out Y _n of the time series data signal calculated by (Equation 4) to 1 / m
27. The flowchart of FIG. 27 is modified so that A conversion is performed and this is used as a compressor drive signal.

It takes a long time (a few minutes to a few tens of minutes) to change the temperature of the room. This is because in the case of an air conditioner, the capacity of the compressor is small and the heat capacity of the room is large compared to this. On the other hand, the rotation response of the fan motor is as short as several hundred ms. Therefore, as shown in FIG. 27, it may be meaningless to generate each drive signal at the same time interval.

For example, the delay time processing is introduced in the flowchart of FIG. 27, and X _n and Y _n are DA converted every 100 ms. In this case, the fan motor 2 can follow the fluctuation of the output irregular time-series data signal, but cannot fluctuate at room temperature. The flowchart of FIG. 28 is a method for avoiding this meaningless operation. It suffices to set m to a value of hundreds to thousands.

Although the time adjustment function, the numerical conversion processing, the linear conversion processing, and the non-linearity correction conversion processing described above are not introduced in the flowcharts of FIGS. 27 and 28 for simplicity, these processings are required. Obviously, it can be introduced.

Further, the push button and switch selection information circuit, the calendar circuit, the external state detection circuit, the activity amount detection circuit, the physiological state detection circuit, and the psychological state detection circuit described above in the present embodiment are provided, and these outputs are used. It is also clear that the coefficient A, B or the setting of the initial values X ₀ , Y ₀ can be changed to select or automatically optimize the wind and temperature fluctuation pattern.

As described above, according to the present embodiment, it is possible to generate complicated two-system time-series data signals by a simple program without requiring a large storage capacity, and at the same time, the rotation speed of the fan motor and the motor of the compressor can be simultaneously generated. By controlling the number of rotations, it is possible to provide the user with comfortable fluctuating wind and room temperature.

FIG. 29 is a block diagram showing a reference example of the present invention. This reference example has an irregular fluctuation in the wind direction as well as the air volume. 29, the same symbols as those in FIG. 1 indicate the same things. In FIG. 29, 20 is a motor drive signal generator, 21 is a vertical louver drive circuit, 22 is a horizontal louver drive circuit, 23 is a vertical louver motor, and 24 is a horizontal louver motor.

Determining the up and down and left and right wind directions is as follows.
This is possible by changing the direction of a series of blades (louvers) (not shown) that are provided at the air outlet of the fan motor 2 and that move vertically and horizontally. The louver motor changes the direction of the louver, and the vertical louver motor 2 moves the vertically movable louver within a predetermined angle range.
3. The left and right louver motor 24 moves the louver movable in the left and right directions within a predetermined angle range.

FIG. 30 is a block diagram showing the structure of the motor drive signal generator 20 shown in FIG. In FIG. 30 , the same symbols as those in FIG. 26 indicate the same things. In addition, 46 is a DA converter.

DA is set to each of the three ports of the MPU 41.
The converters 44, 45 and 46 are connected to output a fan motor drive signal, a vertical louver drive signal and a horizontal louver drive signal, respectively. The respective signals are the fan motor drive circuit 3, the upper and lower louver drive circuits 21, and the left and right louver drive circuits 2, respectively.
Added to 2.

The operation of the motor drive signal generator 20 will be described. Like the fan motor drive signal generator 4, the MPU 41
Signal is generated by the software. 3 in the next (Equation 5)
A non-linear ordinary differential equation of order is shown.

[0152]

[Equation 5]

This is an equation created by Lorenz by simplifying the atmospheric circulation model. This has a coefficient R of 24.
In the range of 74 <R <145, the solution at infinity of the time t does not asymptotically reach a certain value, does not become a periodic solution, and exhibits a strange behavior of continuously moving in a finite region. Therefore, this solution locus is called strange attractor. And this state is chaotic.

This reference example is characterized in that each motor drive signal is generated by using the solution locus of this equation in the chaotic state. Generally, a non-linear ordinary differential equation such as (Equation 5) cannot be solved analytically. Therefore, various methods (eg Runge-Kutta method) are used to find an approximate solution. (Equation 5) is converted into a difference equation with a time step width of Δt and numerically solved. Equation 6 shows the simplest example of this difference equation (convert dX / dt to (X _{n + 1} −X _n ) / Δt).

[0155]

[Equation 6]

The parameter R is set to a certain value within the above range and the initial values X ₀ , Y ₀ and Z ₀ are set to certain values, and the equation 6 is successively applied to X _n , Y _n and Z with an appropriate time step width. go to _n . Figure 3
1 shows an example of the relationship between X _n , Y _n , and Z _n and time (in increments of Δt).

FIG. 32 shows that while solving the above (Equation 6),
It is a flow chart which shows a program of MPU41 for generating a motor drive signal. The parameter R and the initial value are set, and the calculation of (Equation 6) is repeated. In each iteration, the values of X _n , Y _n , and Z _n are output to each DA converter, converted into analog values, and output as drive signals for the fan motor, the upper and lower louvers, and the left and right louvers.

When a positive voltage is applied to the upper and lower louver drive circuit 21, the upper and lower louvers move upward at an angle proportional to the voltage from a predetermined central axis, and conversely, when a negative voltage is applied, the upper and lower louvers move downward. Similarly, the vertical louver drive circuit 21, the vertical louver motor 23, and the vertical louver moving mechanism are designed. Left and right louver drive circuit 22, left and right louver motor 24,
The left and right louver moving mechanism is designed in the same way. And DA
As the converters 45 and 46, those which output positive and negative analog voltages in proportion to the input of the time-series data values of (b) and (c) of FIG. 31 are selected and provided.

When the flow of FIG. 32 is executed by the MPU 41 in this way, the louvers move up and down and left and right in proportion to the time-series data signals of FIGS. Change, that is, fluctuation will be given to the wind direction. The rotation operation of the fan motor 2 has been described above, and will be omitted. The rotation speed of the fan motor 2, that is, the wind speed (air volume) also fluctuates in proportion to the time series data of FIG.

Although the time adjustment function, the numerical conversion processing, the linear conversion processing, and the non-linearity correction conversion processing described above are not introduced in the flowchart of FIG. 32 for simplicity, these processings can be introduced if necessary. Is clear.

[0161] Further, push buttons and switches selection information circuits to the present embodiment described above, the calendar circuit, external state detection circuit, the amount of activity detection circuit, physiological state detection circuit, a psychological state detection circuit is provided, the coefficient these output It is also obvious that the setting of R or the initial values X ₀ , Y ₀ , Z ₀ can be changed to select or automatically optimize the fluctuation pattern of the wind speed and the wind direction.

As described above , according to the present reference example , a complicated program can generate complex three-system irregular time-series data signals without requiring a large storage capacity, and at the same time, the fan motor rotation speed and louver By controlling the position, it is possible to provide the user with a comfortable wind that is close to the natural wind in which the wind volume and the wind direction fluctuate simultaneously.

In the above description, the irregular time-series data signal resulting from the calculation is converted into an analog electric signal by the DA converter and used for actual actuator driving, but the present invention is not limited to this. For example, the time-series data of FIG. 51 may be converted into a binary signal (pulse signal) by comparing with a predetermined value and used as an on / off signal for a motor or the like. In this case, the on time and the off time of the motor change irregularly. By doing so, it is clear that the physical stimulus by the actuator can be fluctuated.

FIG. 33 is a block diagram showing a reference example of the present invention. 33, the same symbols as in FIG. 1 indicate the same things. In addition, in FIG. 33, 25 is a frequency response correction circuit.

In FIG. 33, the fan motor drive circuit 3
FIG. 34 shows an example of frequency response characteristics from the rotation speed to the rotation speed of the fan motor 2. This shows the relationship between the sine wave frequency when the sine wave voltage having a constant amplitude is input to the fan motor drive circuit 3 and the change width of the rotation speed of the fan motor 2.

In FIG. 34, the low frequency (0.1 Hz
In the following), the rotation speed change is proportional to the input amplitude, but at high frequencies (0.5 Hz and above), the rotation speed change range is inversely proportional to the frequency, even though the input amplitude is constant. Becomes smaller. This indicates that at high frequencies, the rotation of the fan motor cannot follow the change in the input signal due to inertial force.

Generally, most of the motor rotation with inertial force or the heat generation phenomenon of the heat generating body with heat capacity has a first-order lag frequency response characteristic as shown in FIG.

The frequency characteristic (spectral slope) of the time-series data signal obtained by the fan motor drive signal generator 4 is the 1 / f characteristic as shown in FIG. 35 (a). When a signal is input from the fan motor drive circuit 3 shown in FIG. 9B to the frequency characteristic system of the rotation speed of the fan motor 2, the resulting frequency characteristic of the rotation speed change is shown in (c) of FIG. ). That is, even if originally intended to obtain a change in the number of revolutions of the characteristic shown in (a) of FIG. 35, that is, a change in wind speed (fluctuation), the result will be the characteristic of (c) in FIG.

The frequency response correction circuit 25 of FIG. 33 is composed of a digital filter or an analog filter,
It is designed in advance to have the characteristic shown in FIG. 35D so as to correct the characteristic shown in FIG. And FIG. 35 (a)
The time-series data signal having the frequency characteristic of is input to the frequency response correction circuit 25, the characteristic is changed to the characteristic of (e) in the figure, and this is input to the fan motor drive circuit 3.

In this way, even if the rotation frequency frequency characteristic is not flat as shown in FIG. 35B, as shown in FIG. 35F, the frequency characteristic of the time-series data signal (see FIG. It is possible to obtain the rotational speed change, that is, the wind speed change (fluctuation), which has the same shape (slope of spectrum) as a)).
That is, it is possible to correctly reflect the spectrum shape of the time-series data signal generated by the fan motor drive signal generator 4 on the spectrum shape of the fluctuation of the target physical stimulus (wind speed).

In the above, the correction of the frequency response characteristic from the fan motor drive circuit to the fan motor rotation speed has been described, but it may be more widely corrected, for example, the change of the wind speed at the position of the user.

The rotation of the fan motor causes wind, and the wind travels through the space and reaches the user. Although the rotation speed is proportional to the wind speed in the immediate vicinity of the fan motor, it becomes more complicated at the position of the user because of turbulence when moving in space. In this case, the frequency characteristics from the fan motor drive circuit to the user's position are measured by observing the wind speed at the general user's position in advance, and the frequency characteristic to correct this is measured as described above. It suffices if the correction circuit 25 is provided.

As described above, according to this reference example , the drive signal generator 4
It is possible to correctly reflect the spectrum shape of the time-series data signal generated in 1 above on the spectrum shape (inclination) of the fluctuation of the physical stimulus (wind) that is the final target.

FIG. 36 is a block diagram showing a reference example of the present invention. In FIG. 36, the same symbols as those in FIGS. 18 and 33 indicate the same things.

This reference example shows the fan motor drive signal generator 4
The spectral slope of the irregular time-series data signal generated by is selected by a push button according to the user's preference. Hereinafter, a method of changing the spectrum slope of the irregular time-series data signal generated by the fan motor drive signal generator 4 will be mainly described.

Mandelbrot's fractional Brownian motion (also called non-integer Brownian motion) is an extension of the Brownian motion, which is one of the natural models, to a non-integer dimension. The idea of the spectral approximation method (also called the Fourier filtering method) for generating this is based on the spectral representation of a sample of the stochastic process X (t).

The process X (t) can be decomposed into the sum of an infinite number of sine functions and cosine functions with respect to the frequency f. And the power (and amplitude) of each is the spectral density S (f)
Depends on Actually, the stochastic process X (t) is expressed by the sum of a finite number of sine functions and cosine functions as shown in the following (Equation 7).

[0178]

[Equation 7]

The coefficient a _{k of the} Fourier transform is the sample point X
There is a one-to-one correspondence with (t _k ). However, t _k = k / N, k
= 0, 1, ..., N-1. k is essentially (Equation 7)
Eq.), S (f) is 1 /
In order to be proportional to (f to the power of β), the coefficient a _k must satisfy the following (Equation 8).

[0180]

[Equation 8]

The spectral approximation method is realized by extracting a random coefficient according to the expected value of (Equation 8). After obtaining the coefficient, X (t) in the time domain is obtained by inverse Fourier transform.

Since only a real number is necessary for the process X (t),
Actually, it is sufficient to extract the real random numbers A _k and B _k that satisfy the following constraint equation (Equation 9).

[0183]

[Equation 9]

Then, X (t) is obtained by the following (Equation 10).

[0185]

[Equation 10]

In the present reference example , the MPU 41 in the fan motor drive signal generator 4 has a spectrum slope of 1 / (f of β by the spectrum approximation method of (Equation 9) and (Equation 10).
Random time series data is generated, and the rotation of the fan motor 2 is drive-controlled by this. FIG. 37 is a flowchart showing the program in this case.

FIG. 38 shows the spectrum of the irregular time series data signal created by the flowchart of FIG. It can be seen that the spectrum slope β changes according to the fractal dimension D.

In FIG. 36, the frequency response correction circuit 25
Is for correcting the frequency characteristic from the fan motor drive circuit 3 described in the previous embodiment to the rotation of the fan motor 2. Since the fan motor drive signal generator 4 is connected to the fan motor drive circuit 3 via the frequency response correction circuit 25, the frequency characteristic of the rotation of the fan motor 2 is determined by the time series generated by the fan motor drive signal generator 4. It becomes the frequency characteristic of the data signal.

For simplicity, it is possible to select 3 shown in FIG.
The description will be given assuming that there are different types of frequency characteristics. The values of the fractal dimension D having the frequency characteristics shown in (a), (b), and (c) of FIG. 38 are stored in advance in the ROM 42 in the fan motor drive signal generator 4. Further, the push button switch 6 is provided with a display showing the above-mentioned fluctuation frequency characteristic.

In FIG. 36, when the user pushes the push button switch 6 having the 1 / f display, the push button information circuit 7 transmits this information to the control circuit 5. Then, the control circuit 5 controls the fan motor drive signal generator 4, temporarily stops the program of the flowchart of FIG. 37, and if a push button different from the fractal dimension D currently being executed is selected, start the flowchart. Returning, the value of the fractal dimension corresponding to the selected push button, in this case, 2 is read from the ROM 42, set to D, and the process proceeds. That is, the generation of an irregular time-series data signal whose frequency characteristic becomes 1 / f is newly started. Thus, the user obtains the wind having the frequency characteristic of the selected fluctuation.

As described above , according to this reference example , the user of the air conditioner can freely select the slope of the fluctuating wind spectrum according to his / her preference. Instead of the push button selection means, the calendar circuit, the outside air state detection circuit, the activity amount detection circuit, the physiological state detection circuit, and the psychological state detection circuit are provided, and the fractal dimension D is automatically selected by these output signals. Obviously it is okay.

FIG. 39 is a block diagram showing a reference example of the present invention. 39, the same symbols as in FIG. 36 indicate the same things. In addition, in FIG. 39, 26 is a variable frequency response correction circuit. In this example , the push button allows the user to select the wind speed fluctuation spectrum shape as desired.

FIG. 40 is a block diagram showing the structure of the variable frequency response correction circuit 26 shown in FIG. In FIG. 40, 51 is a register 52, a multiplier 53, and an adder 54.
, 55 is a coefficient data memory for storing filter coefficients, 56 is an AD converter, and 57 is a DA converter.

The filter coefficient is given as a multiplier of the multiplier and determines the frequency characteristic of the FIR digital filter 51. The AD converter 56 is the fan motor drive signal generator 4
Converts the analog signal output by the
The R digital filter 51 enables digital filter calculation. The DA converter 57 is FIR
The digital filter calculation result of the digital filter 51 is converted into an analog signal.

Now, the time-series data output from the fan motor drive signal generator 4 has the frequency characteristic shown in FIG.
It is assumed that the frequency characteristics up to the number of revolutions have the frequency characteristic shown in FIG. And finally, the change in the rotation speed of the fan motor 2,
That is, it is considered that the frequency characteristics of fluctuations in wind speed are switched to three types of 1 / f, 1 / f ^1.5 , and 1 / f ² .

The filter coefficient of the FIR digital filter 51 having the frequency characteristics shown in (a), (b) and (c) of FIG. 41 is stored in the coefficient data memory 55 in advance.
Further, the push button switch 6 is provided with a display showing the above-mentioned fluctuation frequency characteristic.

When the user pushes the push button switch 6 having the display of 1 / f, the push button information selection circuit 7 transmits this information to the control circuit 5. Then, the control circuit 5 controls the variable frequency response correction circuit 26 to extract from the coefficient data memory 55 the filter coefficient for which the frequency characteristic of the FIR digital filter 51 becomes (a) in FIG.
It is given as a multiplier of 3. Then, the FIR digital filter operates as a filter having the frequency characteristic shown in FIG.

The signal generated by the fan motor drive signal generator 4 (frequency characteristic in FIG. 35 (a)) is input to the variable frequency response correction circuit 26, and its frequency characteristic is corrected. The corrected signal is input to the fan motor drive circuit 3 to rotate the fan motor 2 to generate wind. The rotation characteristic is obtained by multiplying the characteristic shown in FIG. 35 (a), the characteristic shown in FIG. 35 (b), and the characteristic shown in FIG. 41 (a), and as a result, a frequency characteristic of 1 / f is obtained due to wind fluctuation. . As shown in FIG. 41, the variable frequency response correction circuit 26 includes a characteristic for correcting the frequency characteristic from the input of the fan motor drive circuit 3 to the rotation of the fan motor 2.

The same applies to the case where another push button switch is pressed, and the description thereof will be omitted. The variable frequency response correction circuit 26 may be another type of digital filter, for example, an IIR type. And MP without configuring with hardware
Obviously, it can be executed by U's program. MPU4
If the program 1 is used, the DA converter in the fan motor drive signal generator 4 and the variable frequency response correction circuit 2
The AD converter in 6 can be omitted.

Although it may be constituted by an analog filter, in this case, it is necessary to switch elements in order to change the frequency characteristic, so that the circuit configuration becomes complicated. Although the fan motor drive signal generator 4 has been described as generating the irregular time series data signal by the repeated calculation of the nonlinear difference equation as shown in the flowchart of FIG. 3, the present invention is not limited to this. Random numbers are considered an extreme example of chaotic phenomena,
The random number generation may be performed by the program of the MPU 41 to obtain the irregular time series data signal.

As described above, according to this reference example , the user can select the fluctuation frequency characteristic according to his / her preference and obtain a more comfortable wind.

FIG. 42 is a block diagram showing a reference example of the present invention. 42, the same symbols as those in FIGS. 20 and 39 indicate the same things. In this example , the calendar circuit 8
The variable frequency characteristic (inclination of spectrum) of the wind speed described with reference to FIG. 39 is changed by the date and time data output by.

In the coefficient data memory 55 in the variable frequency response correction circuit 26, a plurality of coefficient data for obtaining the frequency characteristics of the wind suitable for the season are stored in advance. For example, four types of representative coefficient data are stored by observing the natural wind spectrum in each of spring, summer, autumn, and winter. For example, the average data of the plateau wind is stored as the one suitable for the summer, and the coefficient data of the average spectrum of the spring wind is stored as the one suitable for the winter.

The control circuit 5 determines the season from the date data output from the calendar circuit 8 and controls the variable frequency response correction circuit 26. Specifically, from the coefficient data memory 55 in the variable frequency response correction circuit 26, the coefficient data for which the frequency characteristic of the wind suitable for the season is obtained is selected.
The digital filter 51 is operated.

As described above, according to this reference example, it is possible to automatically make the fluctuation-like spectrum inclination suitable for the season.

FIG. 43 is a block diagram showing a reference example of the present invention. 43, the same reference numerals as those in FIGS. 21 and 39 denote the same things. In this example , the fluctuation frequency characteristic of the wind speed (spectrum slope) described with reference to FIG. 39 is changed according to the weather with the weather data such as outdoor temperature, humidity, atmospheric pressure, and wind speed output from the outside air state detection circuit 9. is there.

In the coefficient data memory 55 in the variable frequency response correction circuit 26, a plurality of coefficient data for obtaining the frequency characteristics of wind suitable for the weather are stored in advance. For example, four types of coefficient data are stored for fine weather, cloudy weather, rain, and snow. For example, a normal 1 / f that is suitable for sunny weather
The coefficient data for obtaining the characteristics and the coefficient data for obtaining 1 / f ² which is a gentle fluctuation suitable for snow are stored.

The control circuit 5 judges the weather from the data such as the outdoor temperature, humidity, atmospheric pressure, and wind speed output from the outside air condition detection circuit 9, and controls the variable frequency response correction circuit 26.
Specifically, from the coefficient data memory 55 in the variable frequency response correction circuit 26, the coefficient data for obtaining the wind frequency characteristic suitable for the weather is selected, and the FIR digital filter 51 is operated.

As described above, according to this reference example , the spectrum inclination of the fluctuation wind can be automatically made suitable for the weather.

FIG. 44 is a block diagram showing a reference example of the present invention. In FIG. 44, the same symbols as those in FIGS. 22 and 39 indicate the same things. In this example , the activity amount detection circuit 11
The fluctuation frequency characteristic of the wind speed (spectrum slope) described with reference to FIG. 39 is changed according to the activity amount which is the motion amount of the person.

The coefficient data memory 55 in the variable frequency response correction circuit 26 previously stores a plurality of coefficient data for obtaining the frequency characteristics of the wind suitable for the amount of activity of a person. For example, coefficient data that provides flat characteristics suitable for vigorous movements with a large amount of activity, and coefficient data with 1 / f characteristics suitable for a relaxed state with a small amount of activity are stored. .

The control circuit 5 determines the activity state of the user from the activity amount data output from the activity amount detection circuit 11, and controls the variable frequency response correction circuit 26. Specifically, from the coefficient data memory 55 in the variable frequency response correction circuit 26, the coefficient data that provides the frequency characteristics of the wind suitable for the activity state is selected, and the FIR digital filter 51 is operated.

As described above, according to this reference example , the fluctuation-like spectrum inclination can be automatically made suitable for the movement of the user.

FIG. 45 is a block diagram showing a reference example of the present invention. In FIG. 45, the same symbols as those in FIGS. 23 and 39 indicate the same things. In this example , the physiological state detection circuit 1
FIG. 39 shows data such as blood pressure, heart rate, and body temperature that are output by
The fluctuation frequency characteristic of the wind velocity (the slope of the spectrum) described above is changed according to the health condition of the user.

The coefficient data memory 55 in the variable frequency response correction circuit 26 stores in advance a plurality of coefficient data for obtaining the frequency characteristics of the wind suitable for the health condition of the user. For example, the coefficient data that can obtain a normal 1 / f characteristic as being suitable for a healthy state, and the coefficient data that can be obtained as a 1 / f ² characteristic that is loose and fluctuating as being suitable when you have a fever due to illness are stored. I'll do it.

The control circuit 5 determines the health condition of the user from the data such as the body temperature, blood pressure, and heart rate output from the physiological condition detection circuit 12, and controls the variable frequency response correction circuit 26. Specifically, from the coefficient data memory 55 in the variable frequency response correction circuit 26, coefficient data that can obtain the frequency characteristics of the wind suitable for the health condition of the user is selected, and then the FIR data is selected.
The digital filter 51 is operated.

As described above, according to this reference example, it is possible to automatically make the spectrum inclination of the fluctuation wind suitable for the health condition of the user.

FIG. 46 is a block diagram showing a reference example of the present invention. 46, the same reference numerals as those in FIGS. 24 and 39 denote the same things. In this example , the psychological state detection circuit 1
Fig. 3 shows the data such as EEG, heart rate, and sweat rate output from Fig. 3.
The fluctuation frequency characteristic of the wind speed (slope of spectrum) described in 9 is changed according to the psychological state of the user.

In the coefficient data memory 55 in the variable frequency response correction circuit 26, a plurality of coefficient data for obtaining the frequency characteristics of the wind suitable for the psychological state of the user are stored in advance. For example, coefficient data for obtaining a normal 1 / f characteristic suitable for a calm state and coefficient data for obtaining a slow 1 / f ² characteristic for calming as a frustrating state are stored.

The control circuit 5 determines the psychological state of the user from the data of the electroencephalogram of the user, the heart rate, the amount of sweating, etc. output from the psychological state detection circuit 15, and the variable frequency response correction circuit 26
To control. Specifically, the variable frequency response correction circuit 26
From the coefficient data memory 55 therein, the coefficient data for obtaining the wind frequency characteristic suitable for the psychological state of the user is selected, and the FIR digital filter 51 is operated.

As described above, according to the present reference example , the fluctuation-like spectrum inclination can be automatically made suitable for the psychological state of the user.

[0222] In the above reference example, as an example the air conditioning indoor unit, it has been described the operation about the wind and temperature changes,
The present invention is not limited to this. For example, an electric kotatsu using a heater or an electric massage machine using a vibrator can be used in the same manner.
It is clear that you can . In particular, it is desirable to change the physical stimulus depending on the health condition of the user such as the massage machine.

[0223]

As described above, according to the present invention, an irregular time-series data signal is generated by a simple rule (repetitive operation), so that there are many variations (with fluctuations) even if the storage capacity is small. ) A room air conditioner that gives physical stimulation to the user to maintain a comfortable feeling , specifically,
In addition to fluctuations in the wind, allow other fluctuations in the room temperature setting.
A room air conditioner can be provided.

Further, the fluctuation pattern can be set to an optimum one according to the user's preference, season, time zone, weather, health condition, and psychological condition, and a comfortable physical stimulus can be given to the user. Furthermore, the slope (shape) of the power spectrum of the physical stimulus can be set to an optimum one according to the user's preference, season, time zone, weather, and also health and psychological conditions, and the user can be provided with a comfortable physical stimulus. .

[Brief description of drawings]

FIG. 1 is a block diagram showing a configuration of an air conditioner indoor unit .

FIG. 2 is a block diagram showing a configuration of a fan motor drive signal generator in FIG.

FIG. 3 is a flowchart showing an example of a program for generating a fan motor drive signal.

FIG. 4 is a flowchart showing another example of a program for generating a fan motor drive signal.

FIG. 5 is a flowchart showing yet another example of a program for generating a fan motor drive signal.

FIG. 6 is a characteristic diagram showing a relationship between a drive circuit input voltage and a fan motor rotation speed.

FIG. 7 is a flowchart showing another example of a program for generating a fan motor drive signal.

FIG. 8 is an explanatory diagram showing a method of linear conversion processing.

FIG. 9 is a flowchart showing still another example of a program for generating a fan motor drive signal.

FIG. 10 is a characteristic diagram showing a relationship between a drive circuit input voltage and a fan motor rotation speed.

FIG. 11 is an explanatory diagram showing a method of nonlinearity correction conversion processing.

FIG. 12 is a flowchart showing yet another example of a program for generating a fan motor drive signal.

FIG. 13 is a block diagram showing another configuration of a fan motor drive signal generator.

FIG. 14 is a graph showing a time series data signal based on a random number and a reduced map (Levy dust).

FIG. 15 is a flowchart showing another example of a program for generating a fan motor drive signal.

FIG. 16 is a graph showing a time series data signal by a sine circle map.

FIG. 17 is a flowchart showing yet another example of a fan motor drive signal generation program.

FIG. 18 is a block diagram showing a configuration of an air conditioner indoor unit as a reference example of the present invention.

FIG. 19 is a flowchart showing yet another example of a fan motor drive signal generation program.

FIG. 20 is a block diagram showing a configuration of an air conditioner indoor unit as a reference example of the present invention.

FIG. 21 is a block diagram showing a configuration of an air conditioner as a reference example of the present invention.

FIG. 22 is a block diagram showing a configuration of an air conditioner indoor unit as a reference example of the present invention.

FIG. 23 is a block diagram showing a configuration of an air conditioner as a reference example of the present invention.

FIG. 24 is a block diagram showing a configuration of an air conditioner as a reference example of the present invention.

FIG. 25 is a block diagram showing a configuration of an air conditioner as an embodiment of the present invention.

26 is a block diagram showing a configuration of a drive signal generator in FIG. 25.

FIG. 27 is a flowchart showing an example of a drive signal generation program.

FIG. 28 is a flowchart showing another example of the drive signal generation program.

FIG. 29 is a block diagram showing a configuration of an air conditioner indoor unit as a reference example of the present invention.

FIG. 30 is a block diagram showing a configuration example of a drive signal generator.

FIG. 31 is a graph showing a time series data signal of the solution of the Lorenz equation.

FIG. 32 is a flowchart showing another example of a program for generating a motor drive signal.

FIG. 33 is a block diagram showing a configuration of an air conditioner indoor unit as a reference example of the present invention.

FIG. 34 is a characteristic diagram showing frequency response characteristics of fan motor rotation.

FIG. 35 is an explanatory diagram showing a method of frequency response correction.

FIG. 36 is a block diagram showing a configuration of an air conditioner indoor unit as another embodiment of the present invention.

FIG. 37 is a flowchart showing another example of a program for generating a fan motor drive signal.

FIG. 38 is a spectrum diagram showing a spectrum of a fan motor drive signal time-series data signal.

FIG. 39 is a block diagram showing a configuration of an air conditioner indoor unit as a reference example of the present invention.

FIG. 40 is a block diagram showing the configuration of the variable frequency response correction circuit of FIG. 39.

FIG. 41 is a characteristic diagram showing a frequency response of an FIR digital filter.

FIG. 42 is a block diagram showing a configuration of an air conditioner indoor unit as a reference example of the present invention.

FIG. 43 is a block diagram showing a configuration of an air conditioner as a reference example of the present invention.

FIG. 44 is a block diagram showing a configuration of an air conditioner indoor unit as a reference example of the present invention.

FIG. 45 is a block diagram showing a configuration of an air conditioner as a reference example of the present invention.

FIG. 46 is a block diagram showing a configuration of an air conditioner as a reference example of the present invention.

FIG. 47 is a spectrum diagram showing a power spectrum of wind.

FIG. 48 is a block diagram showing a simplified model of a conventional physical quantity control device.

FIG. 49 is an explanatory diagram showing a tree using a recursive function.

FIG. 50 is a bifurcation diagram of the logistic equation.

FIG. 51 is a graph showing time series data of the solution of the logistic equation.

[Explanation of symbols]

1 ... Air conditioner indoor unit, 2 ... Fan motor, 3 ... Fan motor drive circuit, 4 ... Fan motor drive signal generator, 5 ...
Control circuit, 6 ... Push button switch, 7 ... Push button information selection circuit, 8 ... Calendar circuit, 9 ... Air conditioner outdoor unit, 10 ... Outside air state detection circuit, 11 ... Activity amount detection circuit,
12 ... Physiological state detection circuit, 15 ... Psychological state detection circuit, 1
6 ... Drive signal generator, 17 ... Compressor drive circuit, 18 ... Compressor, 19 ... Heat exchanger, 20 ... Motor drive signal generator,
21 ... Vertical louver drive circuit, 22 ... Horizontal louver drive circuit, 23 ... Vertical louver motor, 24 ... Horizontal louver motor, 25 ... Frequency response correction circuit, 26 ... Variable frequency response correction circuit, 41 ... MPU, 42 ... ROM, 43 ... RA
M, 44 ... DA converter, 48 ... Random number generation circuit, 51 ... F
IR digital filter, 55 ... Coefficient data memory

Front page continuation (51) Int.Cl. ⁷ Identification code FI F24D 19/10 F24D 19/10 C F24F 11/02 F24F 11/02 Z G05B 19/02 G05B 19/02 C (72) Inventor Yuji Kawaguchi Tochigi 800 Tomita, Ohira-machi, Shimotsuga-gun, Japan (56) References, Hitachi, Ltd. Living Equipment Division (56) Reference JP-A-3-64696 (JP, A) JP-A-2-223695 (JP, A) JP-A-3-124294 (JP, A) JP-A-63-31657 (JP, A) (58) Fields investigated (Int.Cl. ⁷ , DB name) F24F 11/02

Claims (7)

(57) [Claims] 1. A storage means for storing several kinds of numerical values, and
At least one of the numerical values is set as an initial value or coefficient value.
Time series data of 2 systems by repeatedly calculating the defined equation
And the time series data as a drive signal
Conversion means for converting and the drive signal to the fan motor or
Output to the drive circuit of the compressor motor,
Is a drive signal generator that controls the rotation speed of the compressor motor.
Of the time series data of the two systems, the fan mode
From the number of times the time series data is converted as a
Is also the number of times thinned out and used as the drive signal for the compressor motor.
Room air characterized by converting time series data by
Con. 2. The room air conditioner according to claim 1.
The calendar means for outputting the date and time,
The initial value or coefficient value of the equation is calculated by the calendar means.
To be given depending on the date and time data output from
Characteristic room air conditioner . 3. The room air conditioner according to claim 1.
Data such as outdoor temperature, humidity, atmospheric pressure, and air flow.
An external air condition detecting means for detecting the initial value of the above equation is provided.
Alternatively, the coefficient value is detected by the outside air state detecting means.
Room air characterized by being given depending on the data obtained
Con. 4. The room air conditioner according to claim 1.
To detect the amount of activity that is the movement of a person.
The initial value or coefficient value of the equation is used as the activity amount.
To give depending on the amount of activity detected by the detection means
Room air conditioner characterized by. 5. The room air conditioner according to claim 1.
To detect a person's physiological condition such as blood pressure, heart rate , and body temperature.
Physical condition detection means, and the initial value or coefficient of the equation
The value is the physiological state detected by the physiological state detecting means.
Room air conditioner characterized by giving depending on. 6. The room air conditioner according to claim 1.
To detect a person's psychological state such as brain waves, heart rate, and sweat rate
A psychological state detection means is provided, and the initial value or the coefficient of the equation is
The numerical value is the psychological state detected by the psychological state detecting means.
Room air conditioner characterized by being given depending on the condition. 7. The room air conditioner according to claim 1.
Of the drive circuit of the fan motor or compressor motor
A frequency with a transfer characteristic that is the inverse of the transfer function that represents the frequency response.
It has a number response correction means, and its frequency response correction means
It is characterized in that the drive signal applied to the drive circuit is corrected.
Room air conditioner to collect.