JP2861636B2 - kitchenware - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a cooking appliance for automatic cooking.

[0002]

2. Description of the Related Art Conventionally, this type of cooking utensil, for example, an electronic microwave oven has been configured as shown in FIG. Hereinafter, the configuration will be described.

As shown in FIG. 1, a cooking utensil 1 comprises a heating chamber 2 for storing foods, a cooking means 3 for cooking the foods, and a humidity comprising an absolute humidity sensor for detecting the absolute humidity of the cooking chamber 2. The control means 5 controls the cooking means 3 based on information from the detecting means 4 and the humidity detecting means 4. With such a configuration, automatic cooking has been enabled. For example, in the case of reheating, for example,
There are four categories: rice, miso soup, other general, milk, liquor, leafy vegetables, root vegetables, etc. If you select a category and start cooking, the same category Cooking means 3 in automatic cooking sequence
Is provided in the control means 5. In this automatic cooking sequence, heating is performed for a time obtained by multiplying a time T from the start of cooking to the time when the humidity detecting means 5 detects a certain amount of humidity by a constant K of a selected category. One point of the automatic cooking is that the time T varies depending on the weight of the food. In addition, the constant K has been determined to be an optimal value in a category by performing many cooking experiments.

[0004]

In such a conventional cooking device (here, a microwave oven), the time from the start of cooking until the humidity in the cooking chamber reaches a certain value is measured, and the time is measured for each category. Since the time multiplied by the determined constant K is determined as the cooking time, operation keys are required on the operation unit by the number of categories. In addition, there was considerable variation in the completion of cooking. For example, there is no problem if the reheating of “rice” and “miso soup” are divided into different categories, but if they are the same category, the constant K will be “rice”.
The result was different depending on whether it was mixed with miso soup or "miso soup". The cooked rice was too hot or the miso soup was too warm. In order to solve this problem, the category may be further subdivided, but the number of operation keys increases by the number of subdivisions, and there is a problem that the usability becomes very inconvenient.

[0005] The present invention solves the above-mentioned problems, and the category of the category is estimated by estimating the type of the food based on the environmental physical quantity in the cooking chamber that can be actually measured and detected and the physical quantity unique to the food. An object of the present invention is to provide a microwave oven that can be operated with one button without being required.

[0006]

The present invention has the following configuration to achieve the above object. That is, as first solution means, cooking means for cooking food, environmental physical quantity detection means for detecting an environment around the food, cooking unique physical quantity detection means for detecting a unique physical quantity of the food, A cooking quantity estimating means for estimating the food based on the outputs of the physical quantity detecting means and the cooking specific physical quantity detecting means; and a control means for controlling the cooking means based on the output of the cooking estimating means. .

[0007] Further, as a second solution means, cooking means for cooking food, environmental physical quantity detection means for detecting environmental physical quantities around the food, voltage level detection means for detecting the voltage level of the commercial power supply voltage, Said environmental physical quantity detection means,
A food estimating means for estimating the food based on an output of the voltage level detecting means; cooking of the food based on an output of the food estimating means, an output of the environmental physical quantity estimating means, and an output of the voltage level detecting means; A cooking degree estimating means for estimating the degree and a control means for controlling the cooking means based on an output of the cooking degree estimating means are provided.

Further, as a third solution means, a cooking means for cooking the food, a dish position detecting means for detecting a position of a cooking dish, an environmental physical quantity detecting means for detecting an environmental physical quantity around the cooking dish, and the dish Cooking estimating means for estimating the food based on the output of the position detecting means and the output of the environmental physical quantity detecting means; and the cooking degree of the cooking based on the output of the cooking estimating means and the output of the environmental physical quantity detecting means. And a control means for controlling the cooking means based on the output of the cooking degree estimation means.

Further, as a fourth solution means, a cooking means for cooking food, a dish position detecting means for detecting a position of a cooking dish, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, and a commercial power supply Voltage level detecting means for detecting a voltage level of a voltage; cooking product estimating means for estimating the food based on an output of the dish position detecting means, an output of the environmental physical quantity detecting means, and an output of the voltage level detecting means;
Cooking degree estimating means for estimating the degree of cooking of the food based on the output of the cooking estimating means, the output of the environmental physical quantity estimating means, and the output of the voltage level detecting means; and the cooking based on the output of the cooking degree estimating means. And control means for controlling the means.

Further, as a fifth solution means, a cooking means for cooking the food, an environmental physical quantity detection means for detecting an environmental physical quantity around the food, a unique physical quantity detection means for detecting a unique physical quantity of the food, Cooking estimating means for estimating the food based on the output of the environmental physical quantity detecting means and the output of the eigenphysical quantity detecting means; the output of the cooking estimating means, the output of the environmental physical quantity estimating means, and the eigenphysical quantity detection A cooking degree estimating means for estimating the degree of cooking of the food based on the output of the means, and a control means for controlling the cooking means based on the output of the cooking degree estimating means.

Further, as a sixth solution means, a cooking means for cooking the food, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, a unique physical quantity detecting means for detecting a unique physical quantity of the food, Voltage level detecting means for detecting a voltage level of a commercial power supply voltage; and cooked food estimating means for estimating the cooked food based on an output of the environmental physical quantity detecting means, an output of the unique physical quantity detecting means, and an output of the voltage level detecting means. A cooking degree estimating means for estimating a cooking degree of a food based on an output of the food estimating means, an output of the environmental physical quantity estimating means, an output of the unique physical quantity detecting means, and an output of the voltage level detecting means; And control means for controlling the cooking means based on the output of the cooking degree estimating means.

Further, as a seventh solving means, a cooking means for cooking the food, a dish position detecting means for detecting a position of the cooking dish, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, A unique physical quantity detecting means for detecting a unique physical quantity of an object; a cooking product estimating means for estimating the cooking based on an output of the dish position detecting means, an output of the environmental physical quantity detecting means, and an output of the unique physical quantity detecting means; A cooking degree estimating means for estimating a cooking degree of the cooking based on an output of the cooking estimating means, an output of the environmental physical quantity estimating means, and an output of the unique physical quantity detecting means, based on an output of the cooking degree estimating means. The control means controls the cooking means.

As an eighth solving means, a cooking means for cooking the food, a dish position detecting means for detecting a position of the cooking dish, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, and the cooking means An intrinsic physical quantity detecting means for detecting an intrinsic physical quantity of an object, a voltage level detecting means for detecting a voltage level of a commercial power supply voltage, an output of the dish position detecting means, an output of the environmental physical quantity detecting means, and an output of the intrinsic physical quantity detecting means. A food estimating means for estimating the food based on an output and an output of the voltage level detecting means; an output of the cooking estimating means, an output of the environmental physical quantity estimating means, an output of the unique physical quantity detecting means, and the voltage level Cooking degree estimating means for estimating the degree of cooking of the food based on the output of the detecting means; and controlling the cooking means based on the output of the cooking degree estimating means Cookware consisting of the stage.

Further, the cooking product estimation means and the cooking degree estimation means are obtained by a method of modeling a neural network composed of a plurality of neural elements, and a plurality of fixed food and cooking degree estimation means are provided. And a neural network model having a connection weight coefficient therein. Alternatively, a configuration having a hierarchical neural network model means constructed by combining many layers composed of a plurality of neural elements is adopted.

[0015]

According to the present invention, the following effects can be obtained by the above-described structure. By means of the first problem solving means, the environment information in the cooking chamber from the environmental physical quantity detecting means and the unique physical quantity information from the cooking unique physical quantity detecting means are input to the food estimating means from time to time, thereby estimating the food. The means estimates the food. The cooking degree estimating means estimates the cooking degree based on the estimated cooking information and the environmental physical quantity information from the cooking estimating means, and the control means controls the cooking means based on the output of the cooking degree estimating means.

According to a second aspect of the present invention, there is provided a cooking means for cooking a food, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, and a voltage level detecting means for detecting a voltage level of a commercial power supply voltage. By inputting the outputs of the environmental physical quantity detecting means and the voltage level detecting means to the food estimating means every moment, the food estimating means estimates the food. The cooking degree estimating means estimates the cooking degree based on the estimated cooking information, the environmental physical quantity information and the output of the voltage level detecting means from the cooking estimating means, and the control means controls the cooking means based on the output of the cooking degree estimating means. Control.

According to a third aspect of the present invention, there is provided a cooking means for cooking a dish, a dish position detecting means for detecting a position of a cooking dish, an environmental physical quantity detecting means for detecting an environmental physical quantity around the cooking dish, and the dish. By inputting the output of the position detecting means and the output of the environmental physical quantity detecting means from time to time to the food estimating means, the food estimating means estimates the food.
The cooking degree estimating means estimates the cooking degree based on the estimated cooking information and the environmental physical quantity information from the cooking estimating means, and the control means controls the cooking means based on the output of the cooking degree estimating means.

According to a fourth aspect of the present invention, there is provided a cooking means for cooking food, a dish position detecting means for detecting a position of a cooking dish, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, and a commercial power supply. Voltage level detecting means for detecting a voltage level of a voltage; cooking product estimating means for estimating the food based on an output of the dish position detecting means, an output of the environmental physical quantity detecting means, and an output of the voltage level detecting means;
By inputting the output of the cooked food estimating unit, the output of the environmental physical quantity estimating unit, and the output of the voltage level detecting unit to the cooked food estimating unit every moment, the cooked food estimating unit estimates the cooked food. The cooking degree estimating means estimates the cooking degree based on the estimated cooking information, environmental physical quantity information and voltage level information from the cooking estimating means, and the control means controls the cooking means based on the output of the cooking degree estimating means.

According to a fifth aspect of the present invention, there is provided a cooking means for cooking a food, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, an intrinsic physical quantity detecting means for detecting an intrinsic physical quantity of the food, By inputting the output of the environmental physical quantity detecting means and the output of the unique physical quantity detecting means to the food estimating means every moment, the food estimating means estimates the food. The cooking degree estimating means estimates the cooking degree based on the estimated cooking information, the environmental physical quantity information, and the unique physical quantity information from the cooking estimating means, and the control means controls the cooking means based on the output of the cooking degree estimating means.

According to a sixth aspect of the present invention, there is provided a cooking means for cooking a food, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, an intrinsic physical quantity detecting means for detecting an intrinsic physical quantity of the food, Voltage level detecting means for detecting the voltage level of the commercial power supply voltage, and outputs of the environmental physical quantity detecting means, outputs of the unique physical quantity detecting means, and outputs of the voltage level detecting means are input to the food estimating means every moment. Thereby, the food estimating means estimates the food. The cooking degree estimating means estimates the cooking degree based on the estimated cooked food information, the environmental physical quantity information, the unique physical quantity information and the voltage level information from the cooked food estimating means, and the control means estimates the cooking degree based on the output of the cooking degree estimating means. Control.

According to a seventh aspect of the present invention, there is provided a cooking means for cooking food, a dish position detecting means for detecting a position of a cooking dish, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, and the cooking means. A unique physical quantity detecting means for detecting a unique physical quantity of an object, and an output of the dish position detecting means, an output of the environmental physical quantity detecting means, and an output of the unique physical quantity detecting means are input to the food estimating means every moment. By
The cooking estimating means estimates the cooking. The cooking degree estimating means estimates the cooking degree based on the estimated cooking information, the environmental physical quantity information, and the unique physical quantity information from the cooking estimating means, and the control means controls the cooking means based on the output of the cooking degree estimating means.

According to an eighth aspect of the present invention, there is provided a cooking means for cooking a food, a dish position detecting means for detecting a position of a cooking dish, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, An intrinsic physical quantity detecting means for detecting an intrinsic physical quantity of an object, a voltage level detecting means for detecting a voltage level of a commercial power supply voltage, an output of the dish position detecting means, an output of the environmental physical quantity detecting means, and an output of the intrinsic physical quantity detecting means. By inputting the output and the output of the voltage level detecting means to the food estimating means every moment, the food estimating means estimates the food. The cooking degree estimating means estimates the cooking degree based on the estimated cooking information, the environmental physical quantity information, the specific physical quantity information, and the voltage level information from the cooking estimating means,
The control means controls the cooking means based on the output of the cooking degree estimating means.

Further, according to the ninth solution means, the neural network schematic means constituting the cooked food estimating means and the cooking degree estimating means has the connection weight coefficient already learned in the environment in which the cooked food is cooked. And the degree of cooking during cooking can be estimated.

Further, according to the tenth solution means, the neural network schematic means constituting the cooked food estimating means and the cooking degree estimating means is constructed by combining a plurality of neural elements in multiple layers. The degree can be estimated more accurately.

[0025]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS One embodiment of the present invention will be described below with reference to FIGS. The same components as those in the conventional example are denoted by the same reference numerals, and description thereof is omitted.

(Embodiment 1) In this embodiment, an example in which the present invention is applied to an electronic microwave oven as a cooking appliance will be described.
As shown in FIG. 1, the environmental physical quantity detection means 6 detects the environment in the cooking room. In the present embodiment, the absolute humidity in the cooking chamber is detected, the temperature in the cooking chamber 3 and the gas amount in the cooking chamber are detected, and includes a humidity sensor 6a, a thermistor 6b, and a gas sensor 6c. The gas sensor 6c used in the present embodiment is made of a semiconductor, and detects the amount of gas by a change in odor molecules generated during cooking and heating on the semiconductor surface. The timer 7 counts the time from the start of cooking. The food estimating means 8 is for estimating what the food is based on the outputs of the environmental physical quantity detecting means 6 and the timing means 7. Cooking degree estimating means 9
Estimates the degree of cooking based on the outputs of the food estimating means 8 and the environmental physical quantity detecting means 6. The control means 5 controls the cooking means 3 based on the output of the cooking degree estimating means 9. In the present embodiment, the cooking means 3 includes a microwave supply means 3a and a heater 3b, and is disposed in the cooking chamber 2. A / D converters 10, 11, and 12 convert the outputs of the humidity sensor 6a, thermistor 6b, and gas sensor 6c of the environmental physical quantity detector 6 into digital values. FIG.
FIG. 2 is a configuration diagram showing a key configuration of the operation unit 13.

The means constituting the cooked food estimating means 8 and the cooking degree estimating means 9 cannot be applied to the solution method used in the conventional control method. It is configured in a simulated way. In a method simulating a neural network, a method of using a plurality of connection weight coefficients of a neural network for estimating a food as a fixed table, and a method of leaving a learning function to be adaptable to an environment and a user. There is a way. This embodiment is obtained by a method simulating a neural network, and is the same as the cooked food estimating unit 8 having a neural network model unit having a plurality of fixed connection weight coefficients for estimating the cooked food therein. The obtained cooking degree estimating means 9 is provided.

The temperature in the cooking chamber 2 at the moment of the start of cooking and the change in humidity in the cooking chamber 2 due to steam generated from the cooking substance differ depending on the food.

The fixed connection weighting factor in the neural network for estimating the food is such that when the food to be automatically cooked is actually cooked, the temperature in the cooking chamber 2 and the absolute humidity in the cooking chamber 2 are calculated. Collecting data on how it changes and letting the neural network model learn the correlation between the cooked food, the absolute humidity data in the cooking chamber 2 and the temperature data in the cooking chamber 2 Can be obtained by Similarly, the neural network for estimating the degree of cooking can be obtained by learning the neural network model. The neural network model to be used is described in Document 1 (DE Ramelhart et al., 2 authors, edited by Shunichi Amari, "PDP Model", Sangyo Tosho, 1st Edition).
989), Reference 2 (Kaoru Nakano et al., "Basics of Neurocomputers", published by Corona Co., Ltd., P102, 1990
And Japanese Patent Publication No. 63-55106. Hereinafter, a multi-layer parsing using an error backpropagation method as the most well-known learning algorithm described in Document 1 will be described.
The configuration and operation of a specific neural network model will be described using a Septron as an example.

FIG. 10 is a conceptual diagram of a neural element which is a constituent unit of the neural network model. In FIG. 10, 21
2N are pseudo-synaptic connection converters that simulate nerve synaptic connections, and 2a is a pseudo-synaptic connection converter 21-
An adder for adding the output from 2N, 2b is a set nonlinear function, for example, a sigmoid function with a threshold value h, f (y, h) = 1 / (1 + exp (-y + h)) 1) is a non-linear converter for non-linearly converting the output of the adder 2a. Although not shown for simplicity of the drawing, an input line for receiving a correction signal from the correction means is connected to the pseudo-synaptic coupling converters 21 to 2N and the nonlinear converter 2b. Further, the pseudo synapse connection converters 21 to 2N serve as connection weight coefficients of the neural network model. This neural element has two types of operation modes, a signal processing mode and a learning mode.

Hereinafter, the operation of each mode of the neural element will be described with reference to FIG. First, the operation of the signal processing mode will be described. The neural element has N inputs X1 to X1.
Receives Xn and produces one output. i-th input signal Xi
Is the i-th pseudo-synaptic coupling converter 2 indicated by a square.
In i, it is converted to Wi · Xi. N signals W1.X1 to N1 converted by the pseudo-synaptic coupling converters 21 to 2N
Wn · Xn enters the adder 2a, and the addition result y is sent to the non-linear converter 2b, and becomes the final output f (y, h).

Next, the operation of the learning mode will be described. In the learning mode, the pseudo synapse connection converters 21 to 2
N and the conversion parameters W1 to Wn and h of the nonlinear converter 2b.
To the correction amount {W1} of the conversion parameter from the correction means.
Receiving the correction signals representing Wn and Δh, Wi + ΔWi; i = 1, 2,..., Nh + Δh (Expression 2).

FIG. 11 is a conceptual diagram of a signal converting means constituted by connecting four of the above neural elements in parallel. Needless to say, the following description does not specify the number of neural elements constituting the signal conversion means as four. In FIG. 11, reference numerals 211 to 244 denote pseudo-synaptic coupling converters,
Numerals 201 to 204 denote addition nonlinear converters which combine the adder 2a and the nonlinear converter 2b described in FIG. FIG.
In FIG. 1, the input line for receiving the correction signal from the correction means is connected to the pseudo-synaptic coupling converters 211 to 244 and the addition non-linear converters 201 to 204, although the drawing becomes complicated as in FIG. The pseudo synapse connection converters 211 to 244 also become connection weight coefficients. As for the operation of this signal conversion means, the operation of the neural element described in FIG. 10 is performed in parallel.

FIG. 12 is a block diagram showing the structure of the signal processing means when the error backpropagation method is adopted as the learning algorithm. Reference numeral 31 denotes the above-described signal conversion means. However, here, M neural elements receiving N inputs are arranged in parallel. Numeral 32 denotes a correction means for calculating the correction amount of the signal conversion means 31 in the learning mode. Hereinafter, the operation in the case of learning the signal processing means will be described with reference to FIG. The signal converting means 31 receives N inputs S _in (X) and outputs M outputs S _out (X). The correction means 32 includes an input signal S _in (X) and an output signal S
_out (X), and waits until M error signals δ _i (X) are input from the error calculation means or the signal conversion means at the subsequent stage. The error signal δ _i (X) is input and the correction amount is represented by ΔW _ij = δ _i (X) · S _iout (X) · (1−S _iout (X)) · S _jin (X) (i = 1 to N , J = 1 to M) (Equation 3), and sends the correction signal to the signal conversion means 31. The signal conversion means 31 corrects the conversion parameters of the internal neural elements according to the learning mode described above.

FIG. 13 is a block diagram showing the configuration of a multilayer perceptron using a neural network model.
X, 31Y, and 31Z are signal conversion means composed of K, L, and M neural elements, respectively.
2Z is a correction means, and 33 is an error calculation means. The operation of the multilayer perceptron configured as described above will be described with reference to FIG. In the signal processing unit 34X, the signal conversion unit 31X
_iin (X) (i = 1 to N) and output _Sjout (X)
(J = 1 to K) is output. The correction means 32X outputs the signal S
_iin (X) and the signal _Sjout (X), and the error signal δ
Wait until _j (X) (j = 1 to K) is input. Hereinafter, similar processing is performed in the signal processing units 34Y and 34Z, and the final output S _hout (Z) is output from the signal conversion unit 31Z.
(H = 1 to M) are output. Final output S _hout (Z)
Is also sent to the error calculation means 33. Error calculation means 33
, An error from the ideal output T (T1,..., TM) is calculated based on the square error evaluation function COST (Equation 4), and the error signal δ _h (Z) is corrected. 32Z
Sent to

[0036]

(Equation 1)

Where η is a parameter that determines the learning speed of the multilayer perceptron. Next, when the evaluation function is a square error, the error signal is δh (Z) = − η · (S _hout (Z) −T _h ) (Equation 5). The correction means 32Z calculates the correction amount △ W (Z) of the conversion parameter of the signal conversion means 31Z according to the procedure described above, and calculates the error signal to be sent to the correction means 32Y based on (Equation 6). , The correction signal △ W (Z) to the signal conversion means 31Z, and the error signal δ (Y) to the correction means 32Z.
Send to Y The signal conversion means 31Z outputs the correction signal △ W (Z)
Modify the internal parameters based on. Note that the error signal δ (Y) is given by (Equation 6).

[0038]

(Equation 2)

Here, W _ij (Z) is the signal conversion means 31Z.
Are the conversion parameters of the pseudo-synaptic coupling converter. Hereinafter, similar processing is performed in the signal processing units 34X and 34Y. By repeatedly performing the above procedure called learning, the multilayer perceptron, when given an input, produces an output that approximates the ideal output T well. In the above description, a three-stage multilayer perceptron is used, but the number of stages may be any. Reference 1
The method for correcting the conversion parameter h of the non-linear conversion means in the signal conversion means and the method for increasing the learning speed, which is known as the inertia term, are omitted for simplicity of description. It does not limit the invention described.

As described above, the neural network model means is used to determine the actual food to be automatically cooked and, when the food is cooked, the temperature in the cooking chamber 2, the absolute humidity in the cooking chamber 2, and the cooking chamber 2. It collects data on how the gas amount in the cooking chamber changes, learns the relationship between the cooking product, the absolute humidity data in the cooking chamber 2, the temperature data, and the gas amount data, It is possible to express in a natural manner a method of estimating a cooking item and a cooking degree that are not easy to describe in a rule. However, when learning the cooking degree, the elapsed time from the start of cooking is also learned. In the present embodiment, the information obtained in this way is incorporated into the cooking product estimating means 8 and the cooking degree estimating means 9. Specifically, the cooked food estimating means 8 and the cooking degree estimating means 9 are constituted by using only the signal converting means 31X, 31Y and 31Z of the multi-layer perceptron after the learning is sufficiently completed as the neural network schematic means. .

Next, the data actually learned will be described. FIG. 14 shows a change in the output voltage of the environmental physical quantity detection means 8 when the “pie” is cooked in the oven. FIG.
14A illustrates a change in humidity in the cooking chamber, FIG. 14B illustrates a change in temperature in the cooking chamber, FIG. 14C illustrates a change in gas amount in the cooking chamber, and FIG. Is shown. FIG. 15 shows the change of the output voltage of the environmental physical quantity detection means 6 and the degree of cooking when “sponge cake”, FIG. 16 shows “hamburger”, and FIG. FIGS. 15 (a), 16 (a) and 17 (a) show changes in the humidity in the cooking chamber similarly to FIG. 14 (a), and FIGS. 15 (b), 16 (b) and 17
(B) shows the temperature change in the cooking chamber, and FIGS. 15 (c), 16 (c) and 17 (c) show the gas amount changes in the cooking chamber, and FIGS. 15 (d) and 16 (d). And FIG.
(D) shows the cooking degree in a binary state of cooking not completed and cooking completed.

FIGS. 14 and 15 show that the temperature change in the cooking chamber of the "pie" and the "sponge cake" does not change so much, but the gas emission and the humidity change in the cooking chamber are clearly different. Also, from FIGS. 16 and 17, the change in the gas amount is different between “hamburg” and “gratin”. Experiments were performed on all cooking menus to be automatically cooked, and data were collected. Then, the experimental data was input to the neural network model means for learning.

That is, the neural network network model means learns by inputting the absolute humidity information in the cooking chamber of the environmental physical quantity means 6, the temperature information, the gas quantity information, and the menu of the food as ideal output. Then, the signal conversion means 31X, 31Y, 31Z in the neural network schematic means are established, and they are incorporated in the food estimating means 8 as the neural network schematic means. Similarly, the absolute humidity information, the temperature information, the gas amount information, the cooked food estimation information, and the elapsed time information from the start of cooking for the environmental physical quantity means 6 are also sent to the neural network model means of the cooking degree estimation means 9. And learning the degree of cooking of the food as an ideal output, and the signal conversion means 31X, 31Y, 31Z in the neural network model means.
Are incorporated in the cooking degree estimating means 9 as a neural network model means.

Next, the operation will be described with reference to the block diagram shown in FIG. First, the food is put into the cooking chamber 2. In this embodiment, 12 types of automatic oven cooking menus are considered. The oven cooking mode is selected by the oven cooking key 13a of the operation means 13. Then, cooking is started by the cooking key 13b.
The control means 5 outputs a heating start signal to drive the cooking means 7. The output of the environmental physical quantity detecting means 6 is digitally converted by A / D converting means 10, 11, and 12, and the environmental physical information in the cooking chamber is input to the food estimating means 8 and the cooking degree estimating means 9 every moment. Cooking estimation means 8
Estimates what the food is based on these input signals and information, and outputs the information to the control means 5,
Further, it outputs to the cooking degree estimating means 9 via the control means 5. The cooking estimating means 8 operates to estimate a cooking menu by a neural network learned from a change in environmental physical quantity information in the cooking chamber. Since the cooking menu has been recognized from the estimated cooking information, the control means 5 outputs the estimated cooking information to the cooking degree estimating means 9 as to what the food is. The cooking degree estimating means 9 estimates the cooking degree according to the estimated cooking menu based on the estimated cooked food information, the environmental physical quantity information, and the elapsed time information from the start of cooking from the timing means 7. The cooking degree estimation information of the cooking degree estimation means 9 is output to the control means 5, and the control means 5 controls the cooking means 3 based on the cooking degree estimation information, stops the cooking means 3 upon recognizing the completion.

As described above, according to the present embodiment, cooking is actually performed for a cooking menu to be automatically cooked, and temperature information, humidity information, and gas amount information in the cooking chamber at each moment during cooking are already obtained. Since it has a configuration including a cooking estimating means and a cooking degree estimating means incorporating a neural network model having a plurality of fixed connection weight coefficients of the learned neural network, the menu of the cooking can be recognized, and each menu can be recognized. Since the optimal cooking sequence can be driven, the number of menu selection keys can be reduced as compared with the related art, which is very convenient for usability.

(Embodiment 2) In this embodiment, an example applied to an electronic microwave oven will be described. In particular, in oven cooking, since a heater is used as the cooking means 3, the voltage level of the commercial power supply voltage particularly affects cooking. As shown in FIG. 2, the configuration of the present embodiment is the same as that of the first embodiment, except that a voltage level detecting means 14 for detecting the voltage level of the commercial power supply voltage is provided. By inputting the output of the voltage level detecting means 14 to the food estimating means 8 and the cooking degree estimating means 9, it is possible to more accurately estimate the type of the food and the cooking degree. In addition, when the food to be automatically cooked is actually cooked, the environmental physical quantity (absolute value) around the food is added to the neural network schematic means constituting the food estimation means 8 and the cooking degree estimation means 9 together with the commercial power supply voltage. Data on how humidity (humidity) changes can be collected, and the relationship between the physical quantity of the food and its surroundings (absolute humidity data) and the voltage level of the commercial power supply can be easily learned and described using simple rules. It is possible to express in a natural manner how to estimate a non-cooked food. In the present embodiment, the information obtained in this way is incorporated into the cooking product estimating means 8 and the cooking degree estimating means 9.

As described above, according to the present embodiment, it is said that the commercial power supply voltage level fluctuates from about 90 V to about 110 V. However, the power supply voltage fluctuates in the neural network schematic means constituting the cooking estimation means. Even so, since the relationship between the environmental physical quantity, the commercial power supply voltage level, and the cooking is learned in advance, the accuracy of estimating the type of the cooking and the cooking degree is further improved.

(Embodiment 3) In this embodiment, an example in which the present invention is similarly applied to an electronic microwave oven will be described. The configuration of the present embodiment is the same as that of the first embodiment, as shown in FIG. 3, except that a dish position detecting means 16 for detecting the position of the cooking dish 15 on which the food is placed is provided. Plate position detecting means 16
Is for detecting on which shelf 17 in the cooking chamber 2 the cooking dish 15 is placed. In the present embodiment, a micro switch is used. It is not binding. The food estimating means 8 is for estimating what the food is based on the outputs of the environmental physical quantity detecting means 6, the timing means 7, and the dish position detecting means 14, and the control means 5 outputs the output of the food estimating means 8 The cooking means 3 is controlled based on the cooking means.

Next, the operation will be described with reference to the block diagram shown in FIG. First, the food is placed on the cooking dish 15 and set on the shelf 17 of the cooking chamber 2 determined in advance. In the present embodiment, 12 types of automatic oven cooking menus are considered, and if "pie" or "cakes", the shelf position is below and "hamburger" or "gratin" is used. If so, the shelf position is up. The oven cooking mode is selected by the oven cooking key 13a of the operation means 13. Then, cooking is started by the cooking key 13b. The control means 5 outputs a heating start signal to drive the cooking means 3. The dish position information of the dish position detecting means 14 is the position of the shelf on which the cooking dish is set, and is input to the food estimating means 8. The output of the environmental physical quantity detection means 6 is digitally converted by the A / D conversion means 9, and the information of the environmental physical quantity in the cooking chamber is input to the cooked food estimation means 8 and the cooking degree estimation means 9 every moment. The food estimating means 8 estimates what the food is based on these input signals and information, and outputs the information to the control means 5. Then, the control means 5 outputs the estimated cooked food information to the cooking degree estimating means 9. The cooking estimating means 8 operates so as to roughly classify a cooking category into a cooking category based on the dish position detection information, and to estimate a detailed menu from changes in environmental physical quantity information in the cooking chamber. Since the cooking menu has been recognized from the estimated cooking information, the control means 5 outputs the estimated cooking information to the cooking degree estimating means 9 as to what the food is. The cooking degree estimating means 9 includes estimated cooked food information, environmental physical quantity information,
The cooking degree according to the estimated cooking menu is estimated based on the elapsed time information from the start of cooking from. The cooking degree estimation information of the cooking degree estimation means 9 is output to the control means 5, and the control means 5 controls the cooking means 3 based on the cooking degree estimation information, stops the cooking means 3 upon recognizing the completion. As described above, according to the present embodiment, cooking is actually performed for the cooking menu to be automatically cooked, the shelf position of the set cooking dish, and the temperature information and humidity information in the cooking chamber every moment during cooking. And the amount of gas information are provided with cooking object estimating means incorporating neural network model means having a plurality of learned fixed connection weighting factors. By providing the estimating means, it is possible to drive the optimal cooking sequence corresponding to the menu, so that the number of menu selection keys can be reduced compared to the conventional one, and it is very convenient for usability. Become. Also, the finished quality is improved.

(Embodiment 4) In this embodiment, an example applied to an electronic microwave oven will be described. As shown in FIG. 4, in order to further improve the accuracy of the estimation of the food according to the first embodiment, the voltage level detecting means 14 for detecting the voltage level of the commercial power supply voltage, and the cooking set in the cooking chamber 2 This is realized by inputting information of the dish position detecting means 16 for detecting the position of the dish 15 to the food estimating means 8.
Specific contents have been described in the first, second, and third embodiments, and a description thereof will not be repeated. The effect is as follows.
4 and the dish position detecting means 16, the accuracy of the estimation of the food and the estimation of the degree of cooking are further improved as in the case of the second embodiment.

(Embodiment 5) In this embodiment, an example in which the present invention is applied to a microwave as a cooking appliance will be described. The configuration is shown in FIG. The configuration is almost the same as that of the first embodiment, except that a unique physical quantity detection unit 18 for detecting a physical quantity unique to the food is provided. The cooking estimating means 8 and the cooking degree estimating means 9 are made to learn not only the environmental physical quantity from the cooking that occurs during cooking, but also the physical quantity unique to the cooking.
In the present embodiment, the unique physical quantity detection means 18 detects the weight of the food, and is constituted by a weight sensor or the like. This only needs to be able to detect the weight, and does not restrict the present invention.

The data actually learned will be described. FIG. 18 shows a change in the output voltage of the environmental physical quantity detection means 8 when the “pie” is cooked in the oven. FIG.
18A shows a change in humidity in the cooking chamber, FIG. 18B shows a change in temperature in the cooking chamber, FIG. 18C shows a change in gas amount in the cooking chamber, and FIG. Show change,
FIG. 18E shows the degree of cooking. FIG. 19 shows “sponge cake”, FIG. 20 shows “hamburger”, and FIG. 21 shows “gratin”.
5 shows the output voltage and the degree of cooking. FIGS. 19 (a), 20 (a) and 21 (a) show changes in the humidity in the cooking chamber as in FIG. 18 (a).
(B) and FIG. 21 (b) show the temperature change in the cooking chamber, and FIGS. 19 (c), 20 (c) and 21 (c) show the gas amount change in the cooking chamber, and FIG. 19 (d). , FIG.
(D) and FIG. 21 (d) show the change in weight of the food,
FIGS. 19 (e), 20 (e) and 21 (e) show the degree of cooking in a binary state of unfinished cooking and completed cooking.
From FIGS. 18 and 19, the temperature change in the cooking chamber is not so different between “pie” and “sponge cake”, but the outflow of gas and the humidity change in the cooking chamber are clearly different. There is also a difference in weight change. 20 and FIG.
From "hamburg" to "gratin", the change in gas amount is different, and the change in weight is also different. Experiments were performed on all cooking menus to be automatically cooked, and data were collected. Then, the experimental data was input to the neural network model means for learning.

That is, to the neural network model means, there are four input information of absolute humidity information in the cooking chamber of the environmental physical quantity means 6, temperature information, gas amount information and weight information of the food,
The menu of the food is input and learned as an ideal output, and the signal conversion means 31X, 31Y,
31Z have been established, and they have been incorporated into the food estimation means 8 as a neural network schematic means. Similarly, the environmental physical quantity means 6 is also applied to the neural network model means of the cooking degree estimating means 9.
6 input information of absolute humidity information in the cooking chamber, temperature information, gas amount information, weight information of the cooked food, estimated cooked food information, and elapsed time information from the start of cooking, and the cooking degree of the cooked food as an ideal output. To learn, establish signal conversion means 31X, 31Y, 31Z in the neural network schematic means,
The cooking degree estimating means 9 is used as a neural network model means.
Incorporated in.

As described above, according to the present embodiment, cooking is actually performed for a cooking menu to be automatically cooked, and temperature information, humidity information, gas amount information, and cooked food in the cooking chamber are constantly being cooked. Since the weight information and the elapsed time information from the start of cooking are provided with a cooking object estimating means and a cooking degree estimating means incorporating a neural network schematic means having a plurality of learned fixed connection weight coefficients, the cooking is performed. The menu of things can be recognized, and the optimal workmanship of each menu can be realized.
The number of menu selection keys can be reduced and cooking performance can be improved.

(Embodiment 6) In this embodiment, an example applied to an electronic microwave oven will be described. In particular, in oven cooking, since a heater is used as the cooking means 3, the voltage level of the commercial power supply voltage particularly affects cooking. As shown in FIG. 2, the configuration of the present embodiment is the same as that of the fifth embodiment, except that a voltage level detecting unit 14 for detecting the voltage level of the commercial power supply voltage is provided. The specific contents are the same as those of the second and fifth embodiments, and the same effects as those of the second and fifth embodiments can be obtained.

(Embodiment 7) In this embodiment, an example in which the present invention is similarly applied to an electronic microwave oven will be described. As shown in FIG. 7, the configuration of the present embodiment is the same as that of the fifth embodiment, except that a dish position detecting means 16 for detecting the position of the cooking dish 15 on which the food is placed is provided. The specific contents are the same as those of the third and fifth embodiments, and the same effects as those of the third and fifth embodiments can be obtained.

(Embodiment 8) In this embodiment, an example applied to an electronic microwave oven will be described. As shown in FIG. 8, in order to further improve the accuracy of the estimation of the food according to the fifth embodiment, the voltage level detecting means 14 for detecting the voltage level of the commercial power supply voltage, and the cooking set in the cooking chamber 2 This is realized by inputting information of the dish position detecting means 16 for detecting the position of the dish 15 to the food estimating means 8.
Specific contents have been described in the first, second, third, and fifth embodiments, and a description thereof will be omitted. The effect is that the voltage level detecting means 14 and the dish position detecting means 16 are provided, so that the accuracy of the estimation of the food and the estimation of the cooking degree are further improved as in the case of the second embodiment.

In the embodiment described above, the control means 5, the time keeping means 7, the cooked food estimating means 8 and the cooking degree estimating means 9 are all constituted by 4-bit microcomputers, but these may be constituted by one microcomputer. Is of course possible. In addition, temperature information, humidity information, gas information, and the like are appropriately processed and input as the environmental physical quantity information and the weight information is processed and input as the unique physical quantity information in the cooked food estimating means 9. It is possible to improve the estimation accuracy by changing the processing method without increasing the present invention and increasing the amount of information. Also, in addition to the above as the environmental physical quantity information, temperature information, smoke information, smell information coming out of the food, and color information of the food can also be applied,
In addition, other than the above, the shape of the food,
Information such as volume and height can also be applied. The same effect can be obtained by inputting values processed and processed in advance with each other.
The accuracy is further improved by using a plurality of sensors. Further, in the present embodiment, the microwave oven and the microwave oven having a cooking room have been described. However, the present invention can be applied to a cooking appliance having no cooking room such as a gas table and an electromagnetic cooker. Further, in the present embodiment, the reheating function of the microwave oven and the cooking of sweets in the microwave oven have been described. However, the present invention can also be applied to cooking and preparing prepared dishes and microwave dishes.

[0059]

As is apparent from the above embodiments, according to the present invention, cooking means for cooking food, environmental physical quantity detecting means for detecting the environment around the food, and the intrinsic physical quantity of the food are determined. A cooking-specific physical quantity detecting means for detecting, a cooking-estimating means for estimating the cooking based on outputs of the environmental physical quantity detecting means and the cooking-specific physical quantity detecting means,
Since the control means controls the cooking means based on the output of the food estimating means, the operation keys are used to automatically recognize the type of the food and also recognize the completion of the cooking corresponding to the food. At the same time, the result is improved.

Further, cooking means for cooking the food, environmental physical quantity detecting means for detecting the environmental physical quantity around the food, voltage level detecting means for detecting the voltage level of the commercial power supply voltage, the environmental physical quantity detecting means, A cooking estimating means for estimating the food based on an output of the voltage level detecting means; a cooking degree of the cooking based on an output of the cooking estimating means, an output of the environmental physical quantity estimating means and an output of the voltage level detecting means; And the control means for controlling the cooking means based on the output of the cooking degree estimating means. Therefore, even if the power supply voltage fluctuates, the automatic recognition of the food and the detection of the finished food are possible. Becomes

[0061] Further, cooking means for cooking the food, dish position detecting means for detecting the position of the cooking dish, environmental physical quantity detecting means for detecting the environmental physical quantity around the cooked food, and the output of the dish position detecting means. A food estimating means for estimating the food based on the output from the environmental physical quantity detecting means; and a cooking degree estimating means for estimating the cooking degree of the food based on the output of the food estimating means and the output of the environmental physical quantity estimating means. And control means for controlling the cooking means based on the output of the cooking degree estimating means, so that the food can be recognized for each cooking category, so that the total number of recognized foods increases and the menu to be automatically cooked is selected. Can be increased.

Further, cooking means for cooking the food, dish position detecting means for detecting the position of the cooking dish, environmental physical quantity detecting means for detecting the environmental physical quantity around the food, and detecting the voltage level of the commercial power supply voltage. Voltage level detecting means, cooking estimating means for estimating the food based on the output of the dish position detecting means, the output of the environmental physical quantity detecting means, and the output of the voltage level detecting means, and the output of the cooking estimating means And cooking degree estimating means for estimating the cooking degree of the food based on the output of the environmental physical quantity estimating means and the output of the voltage level detecting means; and control means for controlling the cooking means based on the output of the cooking degree estimating means. Consists of
Even if the power supply voltage fluctuates, it is possible to automatically recognize the type of food and detect the completion of the food, and further, it is possible to recognize the food for each cooking category. be able to.

A cooking means for cooking the food, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, an intrinsic physical quantity detecting means for detecting an intrinsic physical quantity of the food, and an output of the environmental physical quantity detecting means Cooking estimating means for estimating the food based on the output of the characteristic physical quantity detecting means; and cooking based on the output of the cooking estimating means, the output of the environmental physical quantity estimating means, and the output of the specific physical quantity detecting means. Since the cooking degree estimating means for estimating the cooking degree of the food and the control means for controlling the cooking means based on the output of the cooking degree estimating means, the accuracy of the automatic recognition of the type of the food and the detection accuracy of the completion are improved. .

A cooking means for cooking the food, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, an intrinsic physical quantity detecting means for detecting an intrinsic physical quantity of the food, and a voltage level of a commercial power supply voltage. Voltage level detecting means for detecting, food estimating means for estimating the food based on the output of the environmental physical quantity detecting means, the output of the unique physical quantity detecting means, and the output of the voltage level detecting means, and the food estimating means The cooking degree estimating means for estimating the cooking degree of the food based on the output of the environmental physical quantity estimating means, the output of the unique physical quantity detecting means and the output of the voltage level detecting means, and the output of the cooking degree estimating means And control means for controlling the cooking means on the basis of
Even if the power supply voltage fluctuates, the automatic recognition of the food and the completion detection can be performed with higher accuracy.

Further, cooking means for cooking the food, dish position detecting means for detecting the position of the cooking dish, environmental physical quantity detecting means for detecting the environmental physical quantity around the food, and detecting the intrinsic physical quantity of the food. A unique physical quantity detecting means, a food estimating means for estimating the food based on an output of the dish position detecting means, an output of the environmental physical quantity detecting means, and an output of the unique physical quantity detecting means; and Cooking degree estimating means for estimating the degree of cooking of the food based on the output, the output of the environmental physical quantity estimating means and the output of the unique physical quantity detecting means, and control for controlling the cooking means based on the output of the cooking degree estimating means Therefore, the accuracy of the automatic recognition of the type of food and the accuracy of completion detection are improved. In addition, since the food can be recognized for each cooking category, the total number of recognized foods can be increased and the number of menus to be automatically cooked can be increased.

Further, cooking means for cooking the food, dish position detecting means for detecting the position of the cooking dish, environmental physical quantity detecting means for detecting the environmental physical quantity around the food, and detecting the intrinsic physical quantity of the food. Unique physical quantity detection means, voltage level detection means for detecting the voltage level of the commercial power supply voltage, output of the dish position detection means, output of the environmental physical quantity detection means, output of the unique physical quantity detection means, and the voltage level detection means Cooking estimating means for estimating the cooking based on the output of the cooking object; cooking based on the output of the cooking estimating means, the output of the environmental physical quantity estimating means, the output of the unique physical quantity detecting means, and the output of the voltage level detecting means. A cooking degree estimating means for estimating the degree of cooking of the food; and a control means for controlling the cooking means based on an output of the cooking degree estimating means. Type of precision of automatic recognition and ready detection of an object can be improved. In addition, even if the power supply voltage fluctuates, it is possible to automatically recognize the type of food and detect the completion of the food, and since the food can be recognized for each cooking category, the total number of recognized foods increases, and menus that are targeted for automatic cooking are increased. Can be increased.

The food estimating means and the cooking degree estimating means are obtained by learning and modeling a neural network composed of a plurality of neural elements, and are provided with a plurality of fixed connections for estimating the type and cooking degree of the food. Since it has a neural network model having a weight coefficient therein, or has a hierarchical neural network model constructed by combining a plurality of layers composed of a plurality of neural elements, the automatic cooking target With respect to the learned cooking menu, it is possible to estimate the type of the food and the degree of cooking, perform automatic cooking, and provide a convenient cooking device that does not require a cooking menu selection operation and has good cooking performance.

[Brief description of the drawings]

FIG. 1 is a block diagram showing the configuration of a cooking appliance according to one embodiment of the present invention.

FIG. 2 is a block diagram showing the configuration of a cooking appliance according to another embodiment of the present invention.

FIG. 3 is a block diagram showing a configuration of a cooking apparatus according to another embodiment of the present invention.

FIG. 4 is a block diagram illustrating a configuration of a cooking apparatus according to another embodiment of the present invention.

FIG. 5 is a block diagram showing a configuration of a cooking apparatus according to another embodiment of the present invention.

FIG. 6 is a block diagram showing the configuration of a cooking appliance according to another embodiment of the present invention.

FIG. 7 is a block diagram showing the configuration of a cooking appliance according to another embodiment of the present invention.

FIG. 8 is a block diagram showing the configuration of a cooking apparatus according to another embodiment of the present invention.

FIG. 9 is a configuration diagram of an operation unit used for a cooking appliance according to one embodiment of the present invention.

FIG. 10 is a conceptual diagram of a neural element which is a constituent unit of a neural network model used in the cooking appliance.

FIG. 11 is a conceptual diagram of a signal conversion unit composed of neural elements used in the cooking appliance.

FIG. 12 is a block diagram of a signal processing means employing an error back propagation method as a learning algorithm used in the cooking appliance.

FIG. 13 is a block diagram showing a configuration of a multilayer perceptron using a neural network model used in the cooking appliance.

FIG. 14 is a diagram showing an example of experimental data of cooking utensils based on the configuration block diagram of FIG. 1;

FIG. 15 is a view showing another example of the experimental data of the cooking appliance.

FIG. 16 is a view showing another example of the experimental data of the cooking appliance.

FIG. 17 is a view showing another example of the experimental data of the cooking appliance.

FIG. 18 is a diagram showing an example of experimental data of cooking utensils based on the configuration block diagram of FIG. 5;

FIG. 19 is a view showing another example of the experimental data of the cooking appliance.

FIG. 20 is a view showing another example of the experimental data of the cooking appliance.

FIG. 21 is a view showing another example of the experimental data of the cooking appliance.

FIG. 22 is a configuration block diagram of a conventional cooking appliance.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Cookware 3 Cooking means 5 Control means 6 Environmental physical quantity detection means 8 Cooking thing estimation means 9 Cooking degree estimation means 14 Voltage level detection means 15 Cooking dish 16 Dish position detection means 18 Unique physical quantity detection means

──────────────────────────────────────────────────の Continuing from the front page (72) Inventor Kenzo Koji 1006 Kazuma Kadoma, Kadoma, Osaka Prefecture Inside Matsushita Electric Industrial Co., Ltd. No. 1 Matsushita Giken Co., Ltd. (56) References JP-A-4-292714 (JP, A) JP-A-4-297723 (JP, A) (58) Fields investigated (Int. Cl. ⁶ , DB name) F24C 7/02 310

Claims (10)

(57) [Claims] A cooking means for cooking the food; an environmental physical quantity detection means for detecting an environmental physical quantity around the food; a food estimation means for estimating the food based on an output of the environmental physical quantity detection means; A cooking degree estimating means for estimating the cooking degree of the cooking based on an output of the cooking estimating means and an output of the environmental physical quantity estimating means; and a controlling means for controlling the cooking means based on the output of the cooking degree estimating means. Cooking utensils. 2. Cooking means for cooking a food, environmental physical quantity detecting means for detecting an environmental physical quantity around the food, voltage level detecting means for detecting a voltage level of a commercial power supply voltage,
A food estimating means for estimating the food based on the outputs of the environmental physical quantity detecting means and the voltage level detecting means; an output of the cooking estimating means, an output of the environmental physical quantity estimating means, and an output of the voltage level detecting means; A cooking appliance comprising: cooking degree estimating means for estimating a cooking degree of a food based on the cooking degree; and control means for controlling the cooking means based on an output of the cooking degree estimating means. A cooking means for cooking the food; a dish position detecting means for detecting a position of the cooking dish; an environmental physical quantity detecting means for detecting an environmental physical quantity around the food; and an output of the dish position detecting means. A cooking estimating means for estimating the food based on an output from the environmental physical quantity detecting means; and a cooking degree estimating means for estimating a cooking degree of the cooking based on an output of the cooking estimating means and an output of the environmental physical quantity detecting means. And a control means for controlling the cooking means based on the output of the cooking degree estimating means. 4. A cooking means for cooking food, a dish position detecting means for detecting a position of a cooking dish, an environmental physical quantity detecting means for detecting an environmental physical quantity around the cooking food, and detecting a voltage level of a commercial power supply voltage. A voltage level detecting means, a food estimating means for estimating the food based on an output of the dish position detecting means, an output of the environmental physical quantity detecting means, and an output of the voltage level detecting means; Cooking degree estimating means for estimating the degree of cooking of the cooking based on the output, the output of the environmental physical quantity estimating means and the output of the voltage level detecting means, and control means for controlling the cooking means based on the output of the cooking degree estimating means Cooking utensils consisting of 5. A cooking means for cooking a food, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, an intrinsic physical quantity detecting means for detecting an intrinsic physical quantity of the food, and an environmental physical quantity detecting means. A food estimating means for estimating the food based on the output and the output of the unique physical quantity detecting means, based on an output of the cooking estimating means, an output of the environmental physical quantity estimating means, and an output of the unique physical quantity detecting means. A cooking appliance comprising: cooking degree estimating means for estimating a cooking degree of a cook; and control means for controlling the cooking means based on an output of the cooking degree estimating means. 6. A cooking means for cooking a food, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, an intrinsic physical quantity detecting means for detecting an intrinsic physical quantity of the food, and a voltage level of a commercial power supply voltage. Voltage level detecting means for detecting the output of the environmental physical quantity detecting means, the output of the unique physical quantity detecting means, and the output of the voltage level detecting means, and the food estimating means; A cooking degree estimating means for estimating a cooking degree of the food based on an output of the means, an output of the environmental physical quantity estimating means, an output of the unique physical quantity detecting means, and an output of the voltage level detecting means; and an output of the cooking degree estimating means. And a control means for controlling the cooking means based on the cooking tool. 7. A cooking means for cooking food, a dish position detecting means for detecting a position of a cooking dish, an environmental physical quantity detecting means for detecting an environmental physical quantity around the food, and detecting a unique physical quantity of the food. A unique physical quantity detecting means, a food estimating means for estimating the food based on an output of the dish position detecting means, an output of the environmental physical quantity detecting means, and an output of the unique physical quantity detecting means; Cooking degree estimating means for estimating the degree of cooking of the cooked food based on the output of the environmental physical quantity estimating means and the output of the unique physical quantity detecting means, and controlling the cooking means based on the output of the cooking degree estimating means. Cooking utensils comprising control means. 8. Cooking means for cooking the food, dish position detecting means for detecting the position of the cooking dish, environmental physical quantity detecting means for detecting an environmental physical quantity around the food, and detecting a unique physical quantity of the food. An intrinsic physical quantity detecting means, a voltage level detecting means for detecting a voltage level of a commercial power supply voltage, an output of the dish position detecting means, an output of the environmental physical quantity detecting means, an output of the intrinsic physical quantity detecting means, and the voltage level detection. A food estimating means for estimating the food based on an output of the means; an output of the cooking estimating means, an output of the environmental physical quantity estimating means, an output of the unique physical quantity detecting means, and an output of the voltage level detecting means. A cooking appliance comprising: cooking degree estimating means for estimating a cooking degree of a cook; and control means for controlling the cooking means based on an output of the cooking degree estimating means. 9. A method for estimating the degree of cooking and the degree of cooking estimating means, the method comprising: modeling a neural network composed of a plurality of neural elements; and fixing the neural network for estimating the degree of cooking and the degree of cooking. 9. The cooking utensil according to claim 1, further comprising a neural network model means having the determined connection weight coefficient therein. 10. The cooking product estimation means and the cooking degree estimation means,
9. The cooking utensil according to claim 1, further comprising a hierarchical neural network model means constructed by combining a plurality of layers composed of a plurality of neural elements.