JPH05172334A - Cooking implement - Google Patents

Abstract

PURPOSE:To perform an automatic cooking by estimating a material to be cooked from environmental physical amount, eliminating an operation of selecting a cooking menu and improving the finish of cooking. CONSTITUTION:The cooking implement comprises cooking means 3 for cooking a material to be cooked, environmental physical amount detecting means 6 for detecting an environment around the material to be cooked, intrinsic physical amount detecting means 7 for detecting an intrinsic physical amount of the material to be cooked, material to be cooked estimating means 9 for estimating the material to be cooked based on outputs of the means 6, 7, and control means 5 for controlling the means 3 based on an output of the means 9. The means 9 is assembled with neural circuit network model means having a plurality of connecting weight coefficients already cooked, learned and fixed from the menu to be an object to be actually automatically cooked to estimate the material to be cooked and to realize an automatic cooking of an excellent finish and a concentration of automatic cooking keys.

Description

Detailed Description of the Invention

[0001]

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

[0002]

2. Description of the Related Art Conventionally, a cooking utensil of this type, for example, a microwave oven has been constructed as shown in FIG. The configuration will be described below.

As shown in the figure, the cooking utensil 1 includes a cooking chamber 2 for storing a cooking product, a cooking unit 3 (microwave supplying unit) for cooking the cooking product, and a humidity sensor for detecting the relative humidity of the cooking chamber 2. The humidity detecting means 4 is composed of the above, and the control means 5 for controlling the cooking means 3 based on the information from the humidity detecting means 4. In order to automatically cook with such a structure, it is necessary to know the weight of the food, the initial temperature, and the like. For that purpose, the time T from the time when the power is turned on until the output of the humidity detecting means 4 reaches a certain humidity is measured, the weight of the food is estimated based on this time T, and at this time, a constant k peculiar to each food is obtained. The optimum cooking time was the time multiplied by.

In the microwave oven, as shown in FIG.
As shown in FIG. 5, the cooking utensil 1 includes a cooking chamber 2 in which the position of a cooking dish 16 for accommodating a dish is variable, a shelf 15 for placing the cooking dish, a cooking means 3 for cooking the dish, and a cooking chamber 2 inside. The temperature detecting means 4a is composed of a thermistor or the like for detecting the temperature, and the control means 5 is arranged to control the cooking means 3 based on the information from the temperature detecting means 4a. With such a configuration, automatic cooking was possible. Taking oven cooking as an example, menus include cakes, apple pies,
There were pizza, cookies, butter rolls, cream puffs, gratin, foil grill, hamburger, meat grill, barbecue, fish grill. In other words, it was divided into the above 12 menus on the operation unit of the microwave oven. Moreover, the position where the cooking dish is placed is different in each menu, and the three positions of upper, middle, and lower are determined by the menu. For example, for pizza, the plate position is below,
Cream puff is in the middle. The user first decides a menu, sets a cooking dish on which food is placed at a position corresponding to the menu, and presses the cooking start key to start cooking. After that, the control means 5 was equipped with a control sequence program for controlling the cooking means 3 by an automatic cooking sequence corresponding to the menu. In this automatic cooking sequence, the temperature detecting means 4a heats the time T from the start of cooking until the temperature in the cooking chamber reaches a certain temperature by a constant K corresponding to the selection menu. One of the points of automatic cooking is that the time T changes depending on the weight of the food. Further, the constant K has been determined to be the optimum value for the menu by conducting many cooking experiments.

[0005]

In such a conventional cooking utensil, the time from the start of cooking until the humidity in the cooking chamber reaches a certain value is measured, and the time is multiplied by a constant K determined for each category. Since the cooking time has been decided as the cooking time, the operation keys are required in the operation unit for the number of categories. There was also a considerable variation in the cooking results. For example, there is no problem if the reheats of "rice" and "miso soup" are divided into different categories, but if they are made into the same category, the result will differ depending on whether the constant K is adjusted to "rice" or "miso soup". "It was too hot, and" miso soup "was lukewarm. In order to solve this, it is sufficient to subdivide the categories, but the number of operation keys is increased by the number of subdivisions, and there is a problem that usability becomes very poor. The same applies to the microwave oven, and since the automatic cooking sequence corresponding to the menu is provided, the automation for the menu has been realized to some extent. However, since there are many menu keys for selecting a menu, there is a problem in that it is very inconvenient in terms of usability.

The present invention is intended to solve the above-mentioned problems. The menu is estimated by estimating the kind of the cooking object from the physical quantity of the environment around the cooking object that can be actually measured and detected and the dish position detection information on which the cooking dish is placed. It is an object of the present invention to provide an automatic cooking appliance that does not require selection of and can be operated with one button.

[0007]

The present invention has the following structure in order to achieve the above object. That is, as a first solution means, cooking means for cooking the cooked food, environmental physical quantity detecting means for detecting the environment around the cooked food, unique physical quantity detecting means for detecting the unique physical quantity of the cooked food, and the environmental physical quantity The cooked product estimating unit estimates the cooked product based on the output of the detecting unit and the output of the unique physical quantity detecting unit, and the control unit controls the cooking unit based on the output of the cooking product estimating unit.

As a second solution, cooking means for cooking food, environmental physical quantity detection means for detecting the environment around the food, unique physical quantity detection means for detecting the unique physical quantity of the food, and commercial Voltage level detection means for detecting the voltage level of the power supply voltage, and cooked food estimation means for estimating the cooked food based on the output of the environmental physical quantity detection means, the output of the specific physical quantity detection means and the output of the voltage level detection means. And a control means for controlling the cooking means based on the output of the cooking product estimation means.

As a third means for solving the problems, cooking means for cooking the food, plate position detecting means for detecting the position of the cooking plate on which the food is placed, and environmental physical quantity detecting means for detecting the environment around the food And an inherent physical quantity detecting means for detecting an inherent physical quantity of the cooked food, a cooked food for estimating the cooked 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 inherent physical quantity detecting means The estimation means and the control means for controlling the cooking means based on the output of the cooking material estimation means are used.

As a fourth solution, cooking means for cooking food, dish position detecting means for detecting the position of a cooking dish on which the food is placed, and voltage level detection for detecting the voltage level of the commercial power supply voltage Means, environmental physical quantity detection means for detecting the environment around the food, unique physical quantity detection means for detecting the unique physical quantity of the food, output of the plate position detection means, output of the voltage level detection means, and the environment A configuration comprising cooking food estimation means for estimating the cooking food based on the output of the physical quantity detection means and the output of the inherent physical quantity detection means, and control means for controlling the cooking means based on the output of the cooking food estimation means. And

As a fifth means for solving the problems, cooking means for cooking the food, temperature detecting means for detecting the temperature around the food, and humidity detecting means for detecting the humidity around the food are provided.
Weight detection means for detecting the weight of the cooked food, predetermined temperature storage means for storing a predetermined temperature around the cooked food, and output of the temperature detection means until the value stored in the predetermined temperature storage means is reached. A cooking product estimation unit that estimates the cooking product based on the output of the temperature detection unit, the output of the humidity detection unit, and the output of the weight detection unit, and a control that controls the cooking unit based on the output of the cooking product estimation unit It is configured by means.

As a sixth means for solving the problems, a cooking means for cooking the food, a temperature detecting means for detecting the temperature around the food, and a humidity detecting means for detecting the humidity around the food are provided.
Voltage level detection means for detecting the voltage level of the commercial power supply voltage, weight detection means for detecting the weight of the cooked food, predetermined temperature storage means for storing a predetermined temperature around the cooked food, and the voltage level detection The cooking product is estimated based on the output of the temperature detecting means, the output of the humidity detecting means and the output of the weight detecting means until the output of the means and the output of the temperature detecting means reach the stored value of the predetermined temperature storing means. And a control means for controlling the cooking means based on the output of the cooking material estimation means.

As a seventh solving means, a cooking means for cooking the cooked food, a temperature detecting means for detecting the temperature around the cooked food, and a humidity detecting means for detecting the humidity around the cooked food,
A plate position detecting means for detecting the position of the cooking plate on which the food is placed, and a weight detecting means for detecting the weight of the food,
Predetermined temperature storage means for storing a predetermined temperature around a predetermined food product, and of the temperature detection means until the output of the plate position detection means and the output of the temperature detection means reach the stored value of the predetermined temperature storage means. A configuration including a cooking product estimation unit that estimates the cooking product based on an output, an output of the humidity detection unit, and an output of the weight detection unit, and a control unit that controls the cooking unit based on the output of the cooking product estimation unit. And

As an eighth solution means, a cooking means for cooking the food, a temperature detection means for detecting the temperature around the food, and a humidity detection means for detecting the humidity around the food are provided.
Dish position detecting means for detecting the position of the cooking plate on which the food is placed, voltage level detecting means for detecting the voltage level of the commercial power supply voltage, weight detecting means for detecting the weight of the food, and predetermined cooking A predetermined temperature storage means for storing a predetermined temperature around an object; the output of the dish position detection means, the output of the voltage level detection means, and the output of the temperature detection means until the stored value of the predetermined temperature storage means is reached. Cooking estimation means for estimating the cooking based on the output of the temperature detection means, the output of the humidity detection means and the output of the weight detection means, and a control means for controlling the cooking means based on the output of the cooking estimation means It is composed of and.

As a ninth solution means, the control means has a second predetermined temperature storage section for storing a second predetermined temperature lower than the storage value of the predetermined temperature storage means, and the output from the temperature detection means is initially set. When the temperature is higher than the second predetermined temperature, the standby unit that stops the cooking means until the temperature becomes lower than the second predetermined temperature is provided.

Further, the cooking product estimating means is obtained by a method of modeling a neural network composed of a plurality of neural elements, and a nerve having a plurality of fixed connection weight coefficients for estimating a cooking product inside. The configuration has a circuit network schematic means. Alternatively, it is configured to have a hierarchical neural network model means constructed by combining a number of layers composed of a plurality of neural elements.

[0017]

The present invention has the following functions due to the above-mentioned structure. By the first problem solving means, the environmental information around the cooked food and the weight information specific to the cooked food from the environmental physical quantity detecting means are input to the cooked food estimating means from moment to moment so that the cooked food estimating means estimates the cooked food. Then, the control unit recognizes the cooking product based on the cooking product information from the cooking product estimating unit, and controls the cooking unit in a control sequence most suitable for the recognized cooking product.

Further, the second solution means estimates the cooking product moment by moment based on the environmental information around the cooking product from the environmental physical quantity detecting device, the weight information peculiar to the cooking product, and the voltage level information of the commercial power supply voltage from the voltage level detecting device. By inputting to the means, the cooking product estimating means estimates the cooking product, the control means recognizes the cooking product from the cooking product information from the cooking product estimating means, and selects the cooking means in the optimal control sequence for the recognized cooking product. Control.

Further, according to the third solving means, the types of foods are roughly classified according to the information of the plate position detecting means on which the foods are placed, the categories are recognized, and the environmental information around the foods is detected from the environmental physical quantity detecting means. By inputting the weight information peculiar to the cooked product to the cooked product estimating device from moment to moment, the cooked product estimating device estimates the cooked product within the category, and the control device prepares the cooked product from the cooked product estimating device. An object is recognized, and the cooking means is controlled by a control sequence most suitable for the recognized food.

Further, according to the fourth solving means, the type of the cooking product is roughly classified according to the information of the plate position detecting means on which the cooking product is placed, the category is recognized, and the environmental information around the cooking product is detected from the environmental physical quantity detecting means. By inputting the weight information specific to the cooking product and the voltage level information of the commercial power supply voltage from the voltage level detecting unit to the cooking product estimating unit every moment, the cooking product estimating unit estimates the cooking product in the category,
The control means recognizes the cooked food from the cooked food information from the cooked food estimating means, and controls the cooking means in a control sequence most suitable for the recognized cooked food.

[0021] Further, by the fifth solving means, the temperature information, the humidity information and the weight information of the food surrounding the food until the temperature of the food surrounding reaches a predetermined temperature which is set in advance are sent to the food estimating means every moment. By inputting, the cooked product estimating means estimates the cooked food, the control means recognizes the cooked food from the cooked food information from the cooked food estimating means, and controls the cooking means in a control sequence most suitable for the recognized cooked food. ..

Further, the sixth solution provides the voltage level information of the commercial power supply voltage until the temperature around the food reaches a predetermined temperature, the temperature information around the food, the humidity information, and the weight information about the food. , The cooking product estimating unit estimates the cooking product by inputting it into the cooking product estimating unit every moment, and the control unit recognizes the cooking product based on the cooking product information from the cooking product estimating unit, and optimizes the recognized cooking product. The control means controls the cooking means.

Further, according to the seventh solving means, the types of the cooking products are roughly classified according to the information of the plate position detecting means for placing the cooking products,
The category is recognized, and then the temperature information, the humidity information, and the weight information of the food surrounding the food until the temperature around the food reaches a predetermined temperature are input to the food estimating means every moment. By doing so, the cooking product estimation means estimates the cooking products within the category, the control means recognizes the cooking products based on the cooking product information from the cooking product estimation means, and the cooking means in the optimal control sequence for the recognized cooking products. To control.

Further, according to the eighth solving means, the types of the cooked food are roughly classified by the information of the plate position detecting means for placing the cooked food,
Recognize the category, and then occasionally the voltage level information of the commercial power supply voltage until the temperature around the cook reaches a predetermined temperature, temperature information around the cook, humidity information, and weight information of the cook, By inputting into the cooking product estimation means every moment, the cooking product estimation means estimates the cooking products in the category, the control means recognizes the cooking products from the cooking product information from the cooking product estimation means, and the recognized cooking products The cooking means is controlled by the optimal control sequence.

According to the ninth means for solving the problems, the temperature in the cooking chamber is high when trying to cook immediately after cooking,
When the output from the temperature detecting means is initially higher than the second predetermined temperature, the standby portion is provided to stop the cooking means until it becomes lower than the second predetermined temperature. Can be estimated.

According to the tenth solving means, the neural network pattern forming means constituting the cooking food estimating means is provided with the connection weighting coefficient already learned under the environment in which it is used, and the cooking food being cooked is estimated. can do.

According to the eleventh solving means, the neural network model means constituting the cooking food estimating means is constructed by combining a plurality of neural elements in multiple layers, so that the cooking food can be more accurately estimated. You can

[0028]

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

(Embodiment 1) In this embodiment, an example applied to a microwave oven as a cooking utensil will be described. As shown in FIG. 1, the environmental physical quantity detection means 6 detects the environment around the food. In this embodiment, the absolute humidity in the cooking chamber 2 is detected, and is composed of a humidity sensor or the like.
The unique physical quantity detecting means 7 is for detecting a unique physical quantity that the cooking product has, and for detecting the weight of the cooking product in this embodiment, it is composed of a weight sensor or the like. The time measuring means 8 counts the time from the start of cooking. The cooked product estimating means 9 estimates what the cooked food is based on the outputs of the environmental physical quantity detecting means 6, the inherent physical quantity detecting means 7, and the time measuring means 8, and the control means 5 outputs the cooked food estimating means 8. Based on this, the cooking means 3 is controlled. In this embodiment, the cooking means 3 is a microwave supply means and is arranged in the cooking chamber 2. Further, 10 and 11 are AD conversion means for converting the outputs of the environmental physical quantity detection means 6 and the intrinsic physical quantity detection means 7 into digital values. Figure 10
It is a block diagram which showed the key structure of the operation means 12.

Since the resolving method used in the conventional control method cannot be applied to the cooking product estimating means 8, the cooking object estimating means 8 is constructed by a method of modeling an optimal neural network as a multidimensional information processing method. The method of modeling a neural network is
There are a method of using a plurality of connection weighting factors of a neural network for estimating a cooking product as a fixed table, and a method of leaving a learning function so that it can be adapted to an environment and a user. In the present embodiment, a cooking product estimating means 8 having neural network model means for estimating a cooking product, which is obtained by a method of modeling a neural network and has a fixed connection weighting coefficient as a table, is provided. ..

When different cooked foods are cooked, the weight of the cooked foods changes, and the steam generated from the cooked foods changes the humidity in the cooking chamber with the start of cooking.

The coupling weight coefficient fixed in the neural network for estimating the cooking product changes how the weight of the cooking product and the absolute humidity in the cooking chamber change when the cooking product to be automatically cooked is actually cooked. This can be obtained by collecting data as to whether or not to do so and learning the correlation between the weight change data of the food and the absolute humidity data in the cooking chamber by the neural network model means. As a neural network schematic means to be used, reference 1 (DE Ramelhart et al., 2 authors, translated by Shunichi Amari “PDP Model” Sangyo Tosho, 1989)
, 2 (Kaoru Nakano and 7 others, "Basics of Neurocomputers", Corona Publishing Co., Ltd., P102, 1990),
There is one disclosed in Japanese Examined Patent Publication No. 63-55106. Hereinafter, the configuration and operation of a concrete neural network schematic means will be described by taking a multilayer perceptron using an error backpropagation method as the most well-known learning algorithm described in Document 1 as an example.

FIG. 11 is a conceptual diagram of a neural element which is a constituent unit of the neural network model means. In FIG. 11, 21
2N is a pseudo synapse coupling converter simulating nerve synapse coupling, and 2a is a pseudo synapse coupling converter 21-
2B is an adder for adding outputs from 2N, 2b is a set non-linear 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 omitted because the drawing is complicated, an input line for receiving a correction signal from the correction means is connected to the pseudo synapse coupling converters 21 to 2N and the non-linear converter 2b. Further, the pseudo synapse coupling converters 21 to 2N serve as coupling weight coefficients of the neural network schematic unit. This neural element has two types of operation modes, a signal processing mode and a learning mode.

The operation of each mode of the neural element will be described below with reference to FIG. First, the operation of the signal processing mode will be described. Neural elements are N inputs X1
It receives Xn and outputs one output. i-th input signal Xi
Is the i-th pseudo-synaptic coupling converter 2 shown by a square
i is converted to Wi · Xi. N signals W1 · X1 converted by the pseudo synapse coupling converters 21 to 2N
Wn · Xn enters the adder 2a, the addition result y is sent to the nonlinear 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 conversion parameters W1 to Wn and h of the pseudo synapse coupling converters 21 to 2N and the non-linear converter 2b are received, and the correction signals representing the conversion parameter correction amounts ΔW1 to ΔWn and Δh from the correction means are received. , Wi + ΔWi; i = 1, 2, ..., N h + Δh (formula 2).

FIG. 12 is a conceptual diagram of a signal converting means constituted by connecting four neural elements in parallel. The following description does not limit the number of neural elements constituting this signal converting means to four. In FIG. 12, 211
˜244 are pseudo synapse coupling converters 201 to 2
Reference numeral 04 denotes the adder 2a and the non-linear converter 2 described in FIG.
It is an addition nonlinear converter which put together b. 12 is omitted because it is complicated as in FIG.
The input lines receiving the correction signal from the correction means are pseudo synapse coupling converters 211 to 244 and addition nonlinear converters 201 to 201.
It is connected to 204. Pseudo-synaptic coupling converter 21
1 to 244 are also the connection weighting factors. Regarding the operation of the signal converting means, the operations of the neural elements described in FIG. 11 are performed in parallel.

FIG. 13 is a block diagram showing the configuration of the signal processing means when the error back propagation method is adopted as the learning algorithm, and 31 is the above-mentioned signal conversion means. However, here, M neural elements that receive N inputs are arranged in parallel. Reference numeral 32 is 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 Sin (X) and outputs M outputs Sout (X). The correction means 32 includes an input signal Sin (X) and an output signal S
Upon receiving out (X), it waits until the input of M error signals δj (X) from the error calculating means or the signal converting means in the subsequent stage. The error signal δj (X) is input and the correction amount is set to ΔWij = δj (X) .Sjout (X). (1-Sjout (X)). Siin (X) (i = 1 to N, j = 1 to M). ) (Equation 3) is calculated, and the correction signal is sent to the signal conversion means 31. The signal conversion means 31 modifies the conversion parameters of the internal neural elements according to the learning mode described above.

FIG. 14 is a block diagram showing the structure 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, and are 32X, 32Y, and 3X.
2Z is a correction means, and 33 is an error calculation means. The operation of the multi-layer perceptron configured as described above will be described with reference to FIG. In the signal processing means 34X, the signal converting means 31X receives the input Siin.
(X) (i = 1 to N) and output Sjout (X) (j =
1 to K) are output. The correction means 32X receives the signal Siin
(X) and the signal Sjout (X), the error signal δj (X)
Wait until (j = 1 to K) is input. Hereinafter, similar processing is performed in the signal processing means 34Y and 34Z,
Final output Shout (Z) (h = 1) from the signal conversion means 31Z
~ M) is output. The final output Shout (Z) is also sent to the error calculation means 33. In the error calculating means 33, the error with the ideal output T (T1, ..., TM) is calculated based on the square error evaluation function COST (Equation 4), and the error signal .delta.h (Z) is corrected by the correcting means. Sent to 32Z.

[0038]

[Equation 1]

However, 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 · (Shout (Z) −Th) (Equation 5). According to the procedure described above, the correction means 32Z calculates the correction amount ΔW (Z) of the conversion parameter of the signal conversion means 31Z, calculates the error signal to be sent to the correction means 32Y based on (Equation 6), and corrects it. The signal ΔW (Z) is sent to the signal converting means 31Z, and the error signal δ (Y) is corrected by the correcting means 32.
Send to Y. The signal converting means 31Z uses the correction signal ΔW (Z).
Modify internal parameters based on. The error signal δ (Y) is given by (Equation 6).

[0040]

[Equation 2]

Wij (Z) is the signal conversion means 31Z.
Is a conversion parameter of the pseudo synapse coupling converter of. Hereinafter, similar processing is performed in the signal processing means 34X and 34Y. By repeating the above procedure called learning, the multi-layer perceptron produces an output that closely approximates the ideal output T when given an input. In the above description, the three-stage multi-layer perceptron is used, but this may be any number of stages. In addition, reference 1
The modification method of the conversion parameter h of the non-linear conversion means in the signal conversion means and the method of accelerating learning known as the inertia term are omitted for simplification of the description, but this omission is described below. It does not bind the invention.

In this way, the neural network model means actually cooks the object to be cooked, the physical quantity (weight in this embodiment) peculiar to the cooked food, and the environment around the cooked food when the cooked food is cooked. Collect data on how the physical quantity (absolute humidity) changes, learn the relationship between the weight change of the cooked food and the environmental physical quantity (absolute humidity data) around the cooked food, and describe by simple rules It is possible to express in a natural way how to estimate a cooked food that is not easy. In the present embodiment, the cooking product estimation means 9 is configured by incorporating the information thus obtained. Specifically, the signal converting means 31X of the multi-layer perceptron after sufficient learning is completed,
The food product estimating unit 9 is configured by using only 31Y and 31Z as a neural network schematic unit. The data actually learned will be described.

FIG. 15 shows changes in the output voltage of the environmental physical quantity detection means 6 and the intrinsic physical quantity detection means 7 when "rice" is reheated. That is, FIG. 15A shows a change in humidity inside the cooking chamber, and FIG. 15B shows a change in weight every moment. FIG. 16 shows changes in the output voltage of the environmental physical quantity detection means 6 and the intrinsic physical quantity detection means 7 when the “miso soup” is reheated. FIG. 16A shows changes in humidity in the cooking chamber, and FIG. 16B shows changes in weight every moment. ing. 15 and 16 "rice"
And "Miso soup" obviously have different vapor output, so the humidity condition in the cooking chamber is also different. Weight information is necessary because the same "rice" and "miso soup" have different amounts each time they are cooked. Data was collected by conducting such experiments for all cooking menus that are targets of automatic cooking. Then, the experimental data was input to the neural network model means for learning. That is, to the neural network model means, absolute humidity information in the cooking chamber of the environmental physical quantity detection means 6, absolute humidity information one minute before the present time as absolute humidity gradient information, weight information of the intrinsic physical quantity detection means 7, and weight. Weight information one minute before the present time as gradient information, five pieces of information of elapsed time from the start of cooking obtained by the time counting means 8, and the type of food to be input as an ideal output for learning, and neural network model means The signal conversion means 31X, 31Y, 31Z in
They are incorporated into the cooking product estimation 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 to be cooked is put in the cooking chamber 2 and the reheating mode is selected by the reheating key 12a of the operating means 12. Then, cooking is started by the cooking key 12b. The control means 5 outputs a signal to start timing to the timing means 8 and also outputs a cooking start signal to drive the cooking means 3. The timing information of the timing means 8 is the cooking product estimation means 9
Has been entered in. Then, the environmental physical quantity information (absolute humidity information) around the cooking product and the unique physical quantity (weight) peculiar to the cooking product are digitally converted by the AD converting means 10 and 11, and are inputted to the cooking product estimating means 9 every moment. The cooked product estimating means 9 estimates what the cooked food is based on these input signals and information, and outputs the information to the control means 5. Since the cooking menu can be recognized from the estimated cooking information, the control means 5 can execute the cooking sequence according to the cooking menu. In this embodiment, each cooking sequence is the time taken from the start of cooking, which is conventionally performed until the detection of a certain amount of humidity, multiplied by a constant K prepared for each cooking menu. It is time. This control program is provided in the control means 5 and operates so as to control the cooking means 3.

As described above, according to the present embodiment, the neural circuit which actually cooks the cooking menu to be automatically cooked, and learns the weight information of the cooked food and the humidity information of the cooking chamber at that time, is learned. Cooking product estimating means 9 incorporating neural network schematic means having a plurality of fixed coupling weight coefficients of the net
Since it has a configuration that allows you to recognize the menu of cooking items and drive the optimal cooking sequence for each menu, it is possible to improve the cooking state more than before and it is ideal for automatic cooking It will be

(Embodiment 2) In this embodiment, an example applied to a microwave oven will be described. Particularly in oven cooking, since a heater is used as the cooking means 3, the voltage level of the commercial power supply voltage has a particular influence during cooking. As shown in FIG. 2, the configuration of this embodiment is the same as that of the first embodiment, except that it has a voltage level detection means 13 for detecting the voltage level of the commercial power supply voltage. By inputting the output of the voltage level detecting means 13 into the cooking product estimating means 9, the estimation of the cooking product can be made more accurate. In addition, the neural network model means that constitutes the cooking product estimation means 9 includes the commercial physical power supply voltage, the environmental physical quantity (absolute humidity) around the cooking product, and the peculiarity of the cooking product when the cooking product to be automatically cooked is actually cooked. Data on how the physical quantity (weight) of the food changes, and learn the relationship between the physical quantity of the environment around the food (absolute humidity data) and the commercial power supply voltage level, and describe with simple rules It is possible to express in a natural way how to estimate a cooked food. In the present embodiment, the cooking product estimation means 9 is configured by incorporating the information thus obtained.

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

(Embodiment 3) In this embodiment, an example similarly applied to a microwave oven will be described. As shown in FIG. 3, the environmental physical quantity detection means 6 detects the environment inside the cooking chamber. In this embodiment, the absolute humidity in the cooking chamber 2 is detected, the temperature in the cooking chamber 2 and the amount of gas in the cooking chamber 2 are detected, and the humidity sensor 6a, the thermistor 6b, and the gas sensor 6c are included. .. Gas sensor 6 used in this embodiment
c is composed of a semiconductor, and detects the amount of gas by the change of odorous molecules generated during cooking and heating on the semiconductor surface. The unique physical quantity detection means 7 detects the weight of the food as in the first embodiment. The time measuring means 8 counts the time from the start of cooking. The plate position detecting means 14 is a cooking plate 16
It is intended to detect which shelf 15 in the cooking chamber 2 is placed. In the present embodiment, it is constituted by a micro switch, but any device capable of detecting the plate position may be used, which restrains the present invention. is not. The cooked product estimating means 9 estimates what the cooked food is based on the outputs of the environmental physical quantity detecting means 6, the unique physical quantity detecting means 7, the time measuring means 8 and the plate position detecting means 14, and the control means 5 is the cooked food. The cooking means 3 is controlled based on the output of the estimation means 9. In this embodiment, the cooking means 3 comprises a microwave supply means 3a and a heater 3b and is arranged in the cooking chamber 2. further,
Reference numerals 10 and 11 denote A / D conversion means, which are the humidity sensor 6a, the thermistor 6b, and the gas sensor 6c of the environmental physical quantity detection means 6.
And the output of the weight sensor of the intrinsic physical quantity detection means 7 are converted into digital values.

Next, the data learned by the neural network model means constituting the cooking product estimation means will be described. FIG. 17
Shows the change in the output voltage of the environmental physical quantity detection means 6 when the "pie" is cooked in the oven. 17A shows a change in humidity in the cooking chamber, FIG. 17B shows a change in temperature in the cooking chamber, and FIG. 17C shows a change in gas amount in the cooking chamber. FIG. 17D shows the change in weight.
FIG. 18 is a “sponge cake”, FIG. 19 is a “hamburger”, and FIG. 20 is a “gratin”.
Shows the change of the output voltage of. 18 (a) and 19
(A) and FIG. 20 (a) show changes in humidity inside the cooking chamber, as in FIG. 17 (a), and FIGS. 18 (b), 19 (b) and 20 (b) show changes in temperature inside the cooking chamber. Is shown in FIG.
(C), FIG. 19 (c), and FIG. 20 (c) show changes in the amount of gas in the cooking chamber, and FIG. 18 (d), FIG. 19 (d), and FIG.
(D) shows the change in weight of the food. 17 and 1
From 8 on, the temperature change in the cooking chamber does not change much between the "pie" and the "sponge cake", but it is clear that the way gas is emitted and the humidity change in the cooking chamber are different. Also, FIG. 19 and FIG.
The change in gas amount is different between 0 and "hamburger" and "gratin". Experiments were conducted for all cooking menus that are targets of automatic cooking, and data was collected. Then, the experimental data was input to the neural network model means for learning. That is, to the neural network model means, five input information of absolute humidity information in the cooking chamber of the environmental physical quantity means 6, temperature information, gas amount information, food weight information, and plate position information of the cooking plate are input. , Inputting and learning a menu of cooked foods as ideal output, establishing signal conversion means 31X, 31Y, 31Z in the neural network schematic means, and incorporating them into the cooked food estimating means 9 as neural network schematic means There is.

Next, the operation will be described with reference to the block diagram shown in FIG. First, the food to be cooked is placed on the cooking plate 16 and set on the shelf 15 of the predetermined cooking chamber 2. In the present embodiment, 12 types are considered as the target automatic oven cooking menu. If it is "pie" or "cakes", the shelf position is below, and it may be "hamburger" or "gratin". For example, the shelf position is above. The oven cooking key 12c of the operating means 12 is used to select the oven cooking mode. Then, cooking is started by the cooking key 12b. The control means 5 outputs a heating start signal to drive the cooking means 3. Further, the dish position information of the dish position detecting means 14 is the shelf position where the cooking dish is set, and is input to the cooking product estimating means 9. Then, the environmental physical quantity information in the cooking chamber is digitally converted from the output of the environmental physical quantity detecting means 6 by the A / D converting means 10, and the physical quantity peculiar to the cooking product is converted from the output of the unique physical quantity detecting means 7 into the A / D converting means 11.
Is digitally converted by and is input to the cooking product estimation means 9 every moment. The cooking product estimation means 9 estimates what the cooking product is based on these input signals and information,
The information is output to the control means 5. The cooking product estimation means 9 operates to roughly classify the cooking products based on the dish position detection information, recognize the categories, and estimate the detailed menu from the environmental physical quantity information in the cooking chamber and the change in the unique physical quantity.
Since the cooking menu can be recognized from the estimated cooking information, the control means 5 can execute the cooking sequence according to the cooking menu.

As described above, according to the present embodiment, cooking is performed on the cooking menu that is actually the target of automatic cooking, the shelf position of the cooking dish that is set, and the temperature information in the cooking chamber during the cooking, which is constantly changing. And the humidity information, the gas amount information, and the weight information of the cooked food are configured to include the cooked food estimating means 9 in which the neural network schematic means having a plurality of fixed coupling weighting coefficients already learned is incorporated. Since the menus can be recognized and the optimum cooking sequence for each menu can be driven, the number of menu selection keys can be consolidated compared to the conventional one, which is very convenient in terms of usability.

(Embodiment 4) In this embodiment, an example applied to a microwave oven will be described. As shown in FIG. 4, the cooking level estimation means 13 for detecting the voltage level of the commercial power supply voltage and the cooking set in the cooking chamber 2 in order to improve the accuracy of the estimation of the cooking product according to the first embodiment. This is realized by inputting the information of the plate position detecting means 14 for detecting the position of the plate 16 into the cooking product estimating means 9.
Since the specific contents have been described in the first, second, and third embodiments, the description thereof will be omitted. The effect is that the voltage level detecting means 1
3 and the plate position detecting means 14 are provided, the accuracy of estimation of the cooked food is improved as in the case of the second embodiment.

(Embodiment 5) In this embodiment, an example applied to a microwave oven will be described. The configuration is shown in FIG. The humidity detecting means 17 detects the humidity inside the cooking chamber 2.
In this embodiment, the absolute humidity in the cooking chamber 2 is detected, and is composed of a humidity sensor or the like. Reference numeral 18 is a temperature detecting means for detecting the temperature in the cooking chamber 2. The weight detecting means 19 is for detecting the weight of the food and is composed of a weight sensor such as a strain gauge. This sensor is not limited to the present invention as long as it can detect the weight. Predetermined temperature storage means 20
Stores the timing of estimating the food as the temperature in the cooking chamber 2 which can be detected by the temperature detecting means 18. The cooking product estimating means 9 outputs the outputs of the temperature detecting means 18, the humidity detecting means 17, the weight detecting means 19 and the time measuring means 8 when the temperature detected by the temperature detecting means 18 has risen to the stored value of the predetermined temperature storing means 20. Estimate the food based on. The control means 5 determines the cooking temperature, the cooking time, and the cooking method (the energization ratio of the upper heater and the lower heater) based on the output of the cooking product estimating means 9, and controls the heaters 3a and 3b which are the cooking means.

Next, the data learned by the neural network model means constituting the cooking product estimation means 9 will be described. Figure 2
Reference numeral 1 shows changes in the output voltage of the temperature detecting means 18 and the humidity detecting means 17 when the hamburger is cooked. FIG. 22.
Shows changes in the output voltage of the temperature detecting means 18 and the humidity detecting means 17 when the gratin was cooked. Figure 21
21A shows a temperature change in the cooking chamber 2, FIG. 21B shows a humidity change, and FIG. 21C shows a weight change. 22 (a) and 22 (b) and FIG.
FIG. 21 (c) shows FIG. 21 (a), FIG. 21 (b), and FIG.
It corresponds to 1 (c). When the temperature in the cooking chamber reaches 150 ° C., the cooking product is estimated, and thereafter, the control temperature in the cooking chamber 2 and the energization ratio of the heaters 3a and 3b are changed according to the estimated cooking product. Voltage is 15
Data is collected until the voltage reaches 0 ° C. FIG. 21 and FIG. 22 show data up to the completion of cooking after estimating the food. As is clear from FIGS. 21 and 22, the hamburger has a larger area exposed to the air for the volume than the gratin, and the humidity increases faster.

In this way, experiments were conducted on all cooking menus to be automatically cooked, data was collected, and the neural network model means was made to learn. That is, to the neural network model means, temperature information in the cooking chamber of the temperature detecting means 18, humidity information in the cooking chamber of the humidity detecting means 17, and weight detecting means 19
The weight information of the cooked food and the information on the elapsed time from the start of cooking obtained from the time measuring means 8 are simplified, and the temperature detecting means 1
When the detection temperature of 8 is 100 ℃, when it is 125 ℃, it is 150 ℃
At that time, a total of 6 pieces of information of respective humidity information and elapsed time information and information of the cooked food (for example, 1 for gratin and 0 for hamburger) were input and learned as ideal outputs.

As the operation, after the cooking is started, a stored value (for example, 150 ° C.) is input in the predetermined temperature storage means 20 in advance, and when the temperature detected by the temperature detection means 18 reaches the stored value, the temperature detection is performed. The temperature detected by the means 18 is 100 ° C., 12
Humidity information, weight information, and elapsed time information at 5 ° C. and 150 ° C. are extracted and input to the cooking product estimation unit 9 to estimate the cooking product, and the information is output to the control unit 5. The control unit 5 controls the cooking unit 3 (heaters 3a and 3b) based on the cooking product estimation information. That is, when the temperature detected by the temperature detecting means 18 reaches 150 ° C., the output from the food estimating means 9 is received, and if the temperature is 1, the food is gratin.
If it is 0, it is regarded as a hamburger and the control temperature of the cooking chamber 2 and the energization ratio of the heaters 3a and 3b thereafter are determined, and the heating time is determined. Then, the control means 5 performs on / off control of the heaters 3a and 3b so that the temperature in the cooking chamber 2 obtained from the temperature detection means 18 becomes the control temperature.

As described above, according to the present embodiment, it is possible to perform automatic cooking in which the food to be cooked is estimated at the beginning of cooking and the cooking is performed in accordance with the food to be estimated until the completion of cooking. The effect is similar to that of the first embodiment.

(Embodiment 6) In this embodiment, an example applied to a microwave oven will be described. The configuration is shown in FIG. It is almost the same as the fifth embodiment except that it has a voltage level detecting means 13 for detecting the voltage level of the commercial power supply voltage. By inputting the output of the voltage level detecting means 13 into the cooking product estimating means 9,
The accuracy can be increased. The operation is the same as in Example 2.
The same effect as in the second embodiment can be obtained.

(Embodiment 7) As the present embodiment, an example applied to a microwave oven will be described. The configuration is shown in FIG. Almost the same as the fifth embodiment, but the plate position detecting means 14 for detecting the position of the cooking plate 16 set in the cooking chamber 2
This is realized by inputting the information of the above into the cooking product estimation means 9. The specific content is the same as that of the third embodiment, and the same effect as that of the third embodiment can be obtained.

(Embodiment 8) In this embodiment, an example applied to a microwave oven will be described. As shown in FIG. 8, the cooking level of the fifth embodiment is estimated by a voltage level detecting means 13 for detecting the voltage level of the commercial power source voltage and the cooking set in the cooking chamber 2 in order to improve the accuracy. This is realized by inputting the information of the plate position detecting means 14 for detecting the position of the plate 16 into the cooking product estimating means 9. The specific content is the same as in the first, second, and third embodiments, and the same effect as in the fourth embodiment can be obtained.

(Embodiment 9) This embodiment is the same as Embodiment 5, Embodiment 6, Embodiment 7 and Embodiment 8 so that the food can be accurately estimated even if the environmental conditions of the cooking chamber vary. It further improves the accuracy of estimation of. The configuration is shown in FIG. The difference from the fifth embodiment, the sixth embodiment, the seventh embodiment and the eighth embodiment is that the control means 5 is provided with a second predetermined temperature storage unit 21 and a standby unit 22. The second predetermined temperature storage unit 21 stores a value stored in the predetermined temperature storage unit 20 (for example, 15
The temperature (for example, 80 ° C.) lower than 0 ° C. is stored.
When the temperature detected by the temperature detecting means 18 at the start of cooking is higher than the stored value of the second predetermined temperature storage portion 21, the standby portion 22 prohibits energization of the heaters 3a and 3b as the cooking means 3 until the temperature falls below the stored value. , Wait for cooking.

For example, when the user cooks the hamburger immediately after cooking the gratin, the temperature in the cooking chamber is high. At this time, the standby unit 22 is not connected to the heaters 3a and 3b.
Energizing is prohibited. At this time, a fan (not shown) is driven to cool the cooking chamber, and the second predetermined temperature storage unit 2
After the stored value becomes lower than 1, the heaters 3a and 3b are energized to start cooking. FIG. 23 shows changes in the output voltage of the temperature detecting means 18, the humidity detecting means 17, and the weight detecting means 19 when the hamburger is cooked in that case. Figure 23
23A shows a change in temperature inside the cooking chamber, FIG. 23B shows a change in humidity inside the cooking chamber, FIG. 23C shows a change in weight of the cooked product, and FIGS. b) and (c)
Correspond to FIGS. 21 (a), 21 (b) and 21 (c), respectively. In this experiment, the actual cooking is started after the temperature inside the cooking chamber 2 is high immediately after the start of cooking and becomes lower than 80 degrees which is the stored value of the second predetermined temperature storage unit 21. As is clear from FIG. 23 and FIG. 21, after the temperature inside the cooking chamber 2 once drops, the temperature information, the humidity information, and the weight information show substantially the same changes. Therefore, when the detected temperature of the temperature detecting means 17 input to the neural network model means by the cooking product estimating means 8 is 100 degrees, 125 degrees, and 150 degrees, the respective humidity information, weight information, and elapsed time information. The total of 9 pieces of information has the same value, and the cooked food can be correctly estimated. As described above, it is possible to estimate the cooked food even if the environmental conditions of the cooking room vary.

In the above embodiment, the control means 5, the timing means 8 and the cooking product estimation means 9 are all constructed by a 4-bit microcomputer, but it is of course possible to construct them by one microcomputer. Although the temperature information, the humidity information, the 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 to the cooking product estimation means 9, the limitation is not limited to this. It is possible to improve the estimation accuracy by changing the processing method or increasing the amount of information without restricting the present invention. In addition to the above as the environmental physical quantity information, temperature information, smoke information, odor information from the cooked food, color information of the cooked food, etc. can also be applied. Information such as height can also be applied. Also, the same effect can be obtained by inputting values that are mutually processed and processed in advance. Further, although the microwave oven and the microwave oven having the cooking chamber have been described in the present embodiment, the invention can also be applied to cooking utensils having no cooking chamber such as a gas table and an electromagnetic cooker. Furthermore, although the reheating function of the microwave oven and the cooking of sweets in the microwave oven have been described in the present embodiment, the present invention can also be applied to the preparation and preparation of prepared dishes and cooked dishes.

[0064]

As is apparent from the above embodiments, according to the present invention, the cooking means for cooking the food, the environmental physical quantity detecting means for detecting the environmental physical quantity around the food, and the physical quantity peculiar to the food are provided. Inherent physical quantity detecting means for detecting, cooking product estimating means for estimating the cooking material based on the output of the environmental physical quantity detecting means and output of the inherent physical quantity detecting means, and the cooking means based on the output of the cooking food estimating means. Since it comprises control means for controlling, it is possible to recognize what the food is, and at the same time, the cooking means can be optimally controlled for the food, which makes it possible to further improve the finished state of cooking. .. Furthermore, the keys of the operation unit for enabling automatic cooking can be integrated, improving usability.

Further, the cooking means for cooking the food, the environmental physical quantity detecting means for detecting the environment around the food, the unique physical quantity detecting means for detecting the physical quantity peculiar to the food, and the voltage level of the commercial power supply voltage are detected. A voltage level detecting means, a cooked food estimating means for estimating the cooked food based on the outputs of the environmental physical quantity detecting means, the output of the inherent physical quantity detecting means and the output of the voltage level detecting means, and the output of the cooked food estimating means Since the control means controls the cooking means based on the above, the accuracy of estimation of the cooked food is improved even when the power supply voltage fluctuates.

Further, the cooking means for cooking the food, the plate position detecting means for detecting the position of the cooking plate on which the food is placed, the environmental physical quantity detecting means for detecting the environment around the food, and the physical quantity peculiar to the food. Of the inherent physical quantity detection means for detecting the cooking position, the dish position detection means, the output of the environmental physical quantity detection means, and the cooked product estimating means for estimating the cooked food based on the output of the unique physical quantity detecting means, and the cooked food estimating means. Since the cooking means is controlled based on the output, the estimation of the cooked food can be roughly classified according to the plate position first, and then the cooked food can be estimated in detail, so that the accuracy of the cooked food estimation can be further improved. At the same time, by increasing the number of foods that can be estimated, the types of foods that can be automatically cooked can be increased.

Further, the cooking means for cooking the food, the plate position detecting means for detecting the position of the cooking plate on which the food is placed, the voltage level detecting means for detecting the voltage level of the commercial power supply voltage, and the peripheral portion of the food Environmental physical quantity detecting means for detecting environment, unique physical quantity detecting means for detecting physical quantity peculiar to cooking, output of the plate position detecting means, output of the voltage level detecting means, output of the environmental physical quantity detecting means and the unique Since it comprises cooked food estimating means for estimating the cooked food based on the output of the physical quantity detecting means, and control means for controlling the cooking means based on the output of the cooked food estimating means,
The food product estimate can be further improved.

Further, cooking means for cooking the food, temperature detecting means for detecting the temperature around the food, humidity detecting means for detecting the humidity around the food, and weight detecting means for detecting the weight of the food. A predetermined temperature storage means for storing a predetermined temperature around a predetermined cooking product, an output of the temperature detection means and an output of the humidity detection means until the output of the temperature detection means reaches a storage value of the predetermined temperature storage means. The user can select a cooking menu because it comprises a cooking product estimation unit that estimates the cooking product based on the output and the output of the weight detection unit and a control unit that controls the cooking unit based on the output of the cooking product estimation unit. It is possible to provide a cooking utensil that is easy to use and has little variation in the finished product because the operation of selecting is unnecessary and complicated operations are eliminated.

In addition, cooking means for cooking the food, temperature detecting means for detecting the temperature around the food, humidity detecting means for detecting the humidity around the food, and weight detecting means for detecting the weight of the food. A voltage level detecting means for detecting a voltage level of a commercial power supply voltage, a predetermined temperature storing means for storing a predetermined temperature around a predetermined cooking product, an output of the voltage level detecting means and an output of the temperature detecting means. Cooked food estimating means for estimating the cooked food based on the output of the temperature detecting means, the output of the humidity detecting means and the output of the weight detecting means until the stored value of the predetermined temperature storing means is reached, and the cooked food estimating means. And a control means for controlling the cooking means based on the output of 1., the accuracy of estimation of the cooked food is improved even when the power supply voltage fluctuates.

Further, cooking means for cooking the food, temperature detecting means for detecting the temperature around the food, humidity detecting means for detecting the humidity around the food, and weight detecting means for detecting the weight of the food. , A plate position detecting means for detecting the position of the cooking plate on which the food is placed, a predetermined temperature storage device for storing a predetermined temperature around the food, and an output of the plate position detecting device and the temperature detecting device. Cooked food estimating means for estimating the cooked food based on the output of the temperature detecting means, the output of the humidity detecting means and the output of the weight detecting means until the output reaches the stored value of the predetermined temperature storing means; The estimation of the cooked food can be roughly classified according to the position of the plate because the control means controls the cooking means based on the output of the cooked food estimating means. Degrees together with possible further improved, out to increase the estimable cooking the number, it is possible to increase the types of food that the automatic cooking is possible.

Further, cooking means for cooking the food, temperature detecting means for detecting the temperature around the food, humidity detecting means for detecting the humidity around the food, and weight detecting means for detecting the weight of the food. A dish position detecting means for detecting the position of the cooking dish on which the cooked food is placed, a voltage level detecting means for detecting the voltage level of the commercial power supply voltage, and a predetermined temperature storing means for storing a predetermined temperature around the cooked food. The output of the plate position detecting means, the output of the voltage level detecting means, and the output of the temperature detecting means until the output of the temperature detecting means reaches the stored value of the predetermined temperature storing means, and the output of the humidity detecting means. And a cooking product estimation unit that estimates the cooking product based on the output of the weight detection unit, and a control unit that controls the cooking unit based on the output of the cooking product estimation unit. The constant can be further improved.

The control means has a second predetermined temperature storage section for storing a second predetermined temperature lower than the storage value of the predetermined temperature storage means, and when the output from the temperature detection means is initially higher than the second predetermined temperature. Since the standby unit that stops the cooking means until the temperature becomes lower than the second predetermined temperature is provided, even if the environmental conditions of the cooking chamber vary, for example, in continuous use, the estimation of the cooked product can be performed accurately without making an error. Becomes

Further, the cooking product estimating means is a neural network model which has a plurality of fixed connection weight coefficients for estimating a cooking product obtained by learning by modeling a neural network composed of a plurality of neural elements. Since it has a means, or has a hierarchical neural network schematic means constructed by combining a large number of layers composed of a plurality of neural elements, for the learned cooking menu to be the target of automatic cooking, It is possible to estimate cooking items and enable automatic cooking, and to provide easy-to-use cooking utensils that do not require operations for selecting cooking menus.

[Brief description of drawings]

FIG. 1 is a configuration block diagram of a cookware according to an embodiment of the present invention.

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

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

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

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

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

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

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

FIG. 9 is a configuration block diagram of a cookware according to another embodiment of the present invention.

FIG. 10 is a configuration diagram of an operation unit used in the cooking utensil of the embodiment of the present invention.

FIG. 11 is a conceptual diagram of a neural element that is a constituent unit of a neural network schematic means used in the cooking utensil.

FIG. 12 is a conceptual diagram of a signal conversion unit composed of neural elements used in the cooking utensil.

FIG. 13 is a block diagram of a signal processing unit that employs an error back propagation method as a learning algorithm used for the cooking utensil.

FIG. 14 is a block diagram showing a configuration of a multilayer perceptron using a neural network schematic means used in the cooking utensil.

FIG. 15 is a diagram showing an example of experimental data of a cooking utensil based on the configuration block diagram of FIG. 1.

FIG. 16 is a diagram showing another example of experimental data of the cooking utensil.

FIG. 17 is a diagram showing an example of experimental data of a cooking utensil based on the configuration block diagram of FIG. 3.

FIG. 18 is a view showing another example of experimental data of the cooking utensil.

FIG. 19 is a diagram showing another example of experimental data of the cooking utensil.

FIG. 20 is a diagram showing another example of experimental data of the cooking utensil.

FIG. 21 is a diagram showing an example of experimental data of a cooking utensil based on the configuration block diagram of FIG. 5.

FIG. 22 is a diagram showing another example of experimental data of the cooking utensil.

23 is a diagram showing an example of experimental data of the cooking utensil based on the configuration block diagram of FIG. 9. FIG.

FIG. 24 is a configuration block diagram of a conventional cooking utensil.

FIG. 25 is a block diagram of another configuration of the conventional cooking utensil.

[Explanation of symbols]

 1 Cooking Utensils 3 Cooking Means 5 Control Means 6 Environmental Physical Quantity Detecting Means 7 Unique Physical Quantity Detecting Means 9 Cooking Estimating Means 13 Voltage Level Detecting Means 14 Dish Position Detecting Means 16 Cooking Dish 17 Humidity Detecting Means 18 Temperature Detecting Means 19 Weight Detecting Means 20 Predetermined temperature storage means 21 Second predetermined temperature storage means 22 Standby unit

Front page continued (72) Inventor Kenzo Ochi 1006 Kadoma, Kadoma City, Osaka Prefecture Matsushita Electric Industrial Co., Ltd. Within the corporation

Claims (11)

[Claims] 1. A cooking means for cooking food, an environmental physical quantity detection means for detecting the environment around the food, an inherent physical quantity detection means for detecting an inherent physical quantity of the food, and outputs of the environmental physical quantity detection means. And a cooking utensil estimating means for estimating the cooking food based on the output of the inherent physical quantity detecting means, and a control means for controlling the cooking means based on the output of the cooking food estimating means. 2. A cooking means for cooking food, an environmental physical quantity detecting means for detecting an environment around the food, an intrinsic physical quantity detecting means for detecting an inherent physical quantity of the food, and a voltage level of a commercial power supply voltage. Voltage level detecting means for detecting, cooking product estimating means for estimating the cooking product based on the outputs of the environmental physical quantity detecting means, the output of the intrinsic physical quantity detecting means and the output of the voltage level detecting means, and the cooking food estimating means And a control means for controlling the cooking means based on the output of the cooking utensil. 3. Cooking means for cooking food, plate position detection means for detecting the position of a cooking plate on which the food is placed, environmental physical quantity detection means for detecting the environment around the food, and the food An intrinsic physical quantity detecting means for detecting an intrinsic physical quantity,
A cooking product estimation unit that estimates the cooking product based on the output of the plate position detection unit, the output of the environmental physical quantity detection unit, and the output of the intrinsic physical quantity detection unit, and the cooking unit based on the output of the cooking product estimation unit. A cooking utensil consisting of control means for controlling. 4. Cooking means for cooking food, plate position detection means for detecting the position of a cooking plate on which the food is placed, voltage level detection means for detecting the voltage level of the commercial power supply voltage, and the periphery of the food. Environment physical quantity detection means for detecting the environment of, the unique physical quantity detection means for detecting the unique physical quantity of the food, the output of the plate position detection means, the output of the voltage level detection means and the output of the environmental physical quantity detection means, and A cooking utensil comprising: a cooking product estimation unit that estimates the cooking product based on the output of the inherent physical quantity detection unit; and a control unit that controls the cooking unit based on the output of the cooking product estimation unit. 5. Cooking means for cooking food, temperature detection means for detecting the temperature around the food, humidity detection means for detecting the humidity around the food, and weight detection for detecting the weight of the food. Means, a predetermined temperature storage means for storing a predetermined predetermined temperature around the cooking product, an output of the temperature detection means and the humidity detection until the output of the temperature detection means reaches the storage value of the predetermined temperature storage means. A cooking utensil comprising a cooking product estimation unit that estimates the cooking product based on the output of the cooking unit and the output of the weight detection unit, and a control unit that controls the cooking unit based on the output of the cooking product estimation unit. 6. Cooking means for cooking food, temperature detection means for detecting the temperature around the food, humidity detection means for detecting the humidity around the food, and voltage for detecting the voltage level of the commercial power supply voltage. Level detection means, weight detection means for detecting the weight of the food, predetermined temperature storage means for storing a predetermined temperature around the food, output of the voltage level detection means and output of the temperature detection means And a cooked food estimating means for estimating the cooked food based on the output of the temperature detecting means, the output of the humidity detecting means and the output of the weight detecting means until the value reaches the stored value of the predetermined temperature storing means. A cooking utensil comprising a control means for controlling the cooking means based on the output of the estimation means. 7. A cooking means for cooking food, a temperature detecting means for detecting a temperature around the food, a humidity detecting means for detecting humidity around the food, and a position of a cooking plate on which the food is placed. Dish position detecting means for detecting, weight detecting means for detecting the weight of the cooked food, predetermined temperature storing means for storing a predetermined temperature around the cooked food, output of the dish position detecting means and the temperature detection Cooked food estimating means for estimating the cooked food based on the output of the temperature detecting means, the output of the humidity detecting means and the output of the weight detecting means until the output of the means reaches the stored value of the predetermined temperature storing means, A cooking device comprising: a control unit that controls the cooking unit based on the output of the cooking product estimation unit. 8. A cooking means for cooking food, a temperature detecting means for detecting a temperature around the food, a humidity detecting means for detecting humidity around the food, and a position of a cooking plate on which the food is placed. Dish position detecting means for detecting, voltage level detecting means for detecting the voltage level of the commercial power supply voltage, weight detecting means for detecting the weight of the food, and predetermined temperature for storing a predetermined temperature around the predetermined food. Storage means, output of the plate position detection means, output of the voltage level detection means, and output of the temperature detection means until the output of the temperature detection means reaches the storage value of the predetermined temperature storage means, and the humidity detection means And a control means for controlling the cooking means on the basis of the output of the cooked product estimating means. 9. The control means has a second predetermined temperature storage section for storing a second predetermined temperature lower than the storage value of the predetermined temperature storage means, and the output from the temperature detection means is initially higher than the second predetermined temperature. 10. The cooking utensil according to claim 5, further comprising a standby portion for stopping the cooking means until the temperature becomes lower than the second predetermined temperature. 10. The cooking food estimation means is a neural network which has a plurality of fixed connection weight coefficients inside which is obtained by learning by modeling a neural network composed of a plurality of neural elements and estimates a cooking food. The cooking utensil according to any one of claims 1 to 8, which has a schematic means. 11. The cooked product estimating means comprises hierarchical neural network schematic means constructed by combining a number of layers composed of a plurality of neural elements.
9. The cooking utensil according to claim 8.