JP3110505B2 - 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 automatically cooking in a microwave oven, gas oven, roaster or the like.

[0002]

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

[0003] As shown in the figure, a heating chamber 1 is for cooking foods therein. The heating chamber 1 is provided with a heating supply means (heater) 2 for heating the foods, and a temperature detecting means comprising a thermistor for detecting the internal temperature. 3 are provided. The control means 4 controls the heating supply means 2 based on information from the temperature detection means 3. In order to automatically cook with such a configuration, it is necessary to know the weight of the food, the initial temperature, and the like. For this purpose, the output voltage gradient of the temperature detecting means 3 is measured for several minutes from the time of turning on the power. If the gradient is steep, the weight of the food is determined to be light, and if the gradient is gentle, it is determined that the weight is heavy. Is set as the optimal cooking time. FIG. 12 shows the output voltage characteristics of the temperature detecting means 3. FIG.
2 (a) shows a light weight, and FIG. 12 (b) shows a heavy weight. He had done numerous cooking experiments to determine the constant.

[0004]

In such a conventional cooking utensil (here, roaster), a gradient of the ambient temperature in the heating chamber 1 is detected, and based on the detected gradient, a cooking product (for example, a fish grill) is detected. Since the cooking time was determined by judging the weight of (1), there was considerable variation in the completion of cooking. For example, the initial temperature in the heating chamber 1 is not always low, and when cooking is performed immediately after cooking, the initial temperature in the heating chamber 1 becomes very high. In this case, when heavy food is cooked, the output voltage characteristics of the temperature detecting means 3 become as shown in FIG. 13, and the temperature in the heating chamber 1 decreases momentarily. This is because the heat in the heating chamber 1 is absorbed by the cooked food because the temperature in the heating chamber 1 is high even when cooking is started. In such a case, it was difficult to determine the optimum cooking time by the above-mentioned method. In addition, heating supply means 2
Is composed of a heater, the fluctuation of the commercial power supply voltage has a considerable effect on the finished cooking. That is,
Depending on the environment at the start of cooking (type of food, initial temperature of heating room 1, initial temperature of food, power supply voltage, etc.)
There was a problem that the finish of the cooking varied considerably. Furthermore, in terms of burning of fish and the like, it is important that the baked state of the surface is important, but it is said that the temperature rise inside the fish is most preferably 60 to 70 ° C. In view of this point, there is a problem that it is very difficult to detect the completion of cooking from the viewpoint of the degree of burning of the surface and the rise in temperature inside.

SUMMARY OF THE INVENTION The present invention solves the above-mentioned problems, and estimates in real time the environmental physical quantity in a heating room that can actually measure and detect the temperature of a cooking product, indirectly detects the completion of the cooking product, and performs cooking. The purpose is to improve the finished state.

[0006]

SUMMARY OF THE INVENTION In order to achieve the above object, the present invention provides a heating chamber for storing foods for cooking, a heating supply means for heating the foods, and an environment in the heating chamber. Environmental physical quantity detecting means for detecting, voltage level detecting means for detecting a power supply level of a commercial power supply voltage, a surface temperature of the food based on an output of the environmental physical quantity detecting means and an output of the voltage level detecting means, a central temperature, Temperature estimation means for estimating at least one of the back surface temperatures; and control means for controlling the heating and supply means based on the output of the temperature estimation means, wherein the temperature estimation means comprises a plurality of neural elements. Equipped with a hierarchical neural network model having a plurality of connection weighting factors of a fixed neural network for estimating the temperature of a food obtained by a method simulating a neural network to be performed It is a means for solving the problems that.

[0007]

According to the present invention, by actually inputting the environmental information in the heating chamber from the environmental physical quantity detecting means and the voltage level of the commercial power supply voltage from the voltage level detecting means to the temperature estimating means, the present invention realizes actual cooking. The temperature estimating means having the neural network model means having the learning function of learning the actual cooking environment to be performed and having a fixed connection weight coefficient therein, is at least one of the surface temperature, the center temperature, and the back surface temperature of the food. One is estimated every moment. The control means controls the heating supply means (heater in the case of an electric roaster) based on the output of the temperature estimating means and indirectly detects a rise in the temperature of the cooked food, whereby it is possible to recognize the optimum completion of cooking.

[0008]

DESCRIPTION OF THE PREFERRED EMBODIMENTS One embodiment of the present invention will be described below with reference to FIGS. 1 and 2 for an electric roaster. In addition,
Components having the same configuration as the conventional example are denoted by the same reference numerals, and description thereof is omitted.

As shown in the figure, environmental physical quantity detecting means 5
Is for detecting the environment in the heating chamber 1, and in this embodiment, is configured by a thermistor or the like, and detects the ambient temperature in the heating chamber 1. The voltage level detecting means 6 detects the voltage level of the commercial power supply voltage. The timer 7 counts the time from when the power is turned on. The operation means 8 includes a category selection key 9 for selecting a category of the food and a cooking start key.
And a cooking key 10 for stopping. The temperature estimating means 11 estimates the surface temperature, the center temperature, and the back surface temperature of the food based on the outputs of the environmental physical quantity detecting means 5, the voltage level detecting means 6, the timing means 7, and the category selection key 9, and the controlling means. Reference numeral 12 controls the heating supply unit 2 based on the output of the temperature estimation unit 11. The display means 13 comprises a fluorescent display tube, and displays the remaining time of cooking and the like.
The A / D converters 14 and 15 convert the outputs of the environmental physical quantity detector 5 and the voltage level detector 6 into digital quantities. Operation means 8 and display means 1
3 is configured as shown in FIG.

[0010] As a means constituting the temperature estimating means 11, since a solution method used in the conventional control method cannot be applied, the temperature estimating means 11 is constructed by a method simulating a neural network which is optimal as a multidimensional information processing method. I have. In the method simulating the neural network, a method of using a plurality of connection weighting factors of the neural network as a fixed table for estimating the temperature of the food (surface temperature, center temperature, back surface temperature) and a learning function are left. There are ways to adapt to the environment and the user. In this embodiment, there is provided a temperature estimating unit 11 having a neural network model having a fixed connection weight coefficient therein for estimating the temperature of a food obtained by a method imitating a neural network.

[0011] Factors that affect the finished food include the initial temperature in the heating chamber, the initial temperature of the food, the type (category) of the food, and the voltage level of the commercial power supply. The finished food greatly varies depending on these factors.

[0012] The connection weighting factor fixed in the neural network for estimating the temperature of the food is determined by the surface temperature of the food when cooking in an environment of actual cooking (an environment in which various factors described above are combined), Collects data on how the center temperature, backside temperature, and ambient temperature in the heating room change, and correlates the environmental data with the ambient temperature data in the heating room and the food temperature (front, center, and backside) data. It can be obtained by learning a neural network model. Reference 1 (DE Ramelhart et al., 2nd author, Shunichi Amari's "PDP model", 1989) and reference 2 (Kaoru Nakano et al., "Basics of Neurocomputers") Corona Publishing Co., P102, 1990), JP-B-63-55106
There are those shown in the official gazettes. Hereinafter, the configuration and operation of specific neural network model means will be described using a multilayer perceptron using an error back propagation method as an example of the most well-known learning algorithm described in Document 1.

FIG. 3 is a conceptual diagram of a neural element which is a constituent unit of the neural network schematic means. In FIG.
N is a pseudo-synaptic connection converter that simulates a synaptic connection of a nerve, and 2a is a pseudo-synaptic connection converter 21-2N
2b is a set nonlinear function, for example, a sigmoid function with a threshold value h, f (y, h) = 1 / (1 + exp (−y + h)) (Equation 1) ) Is a non-linear converter for nonlinearly 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.

The operation of each mode of the neural element will be described below with reference to FIG. First, the operation in the signal processing mode will be described. The neural element has N inputs X1 to X
n and outputs 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 in the learning mode will be described. In the learning mode, the conversion parameters W1 to Wn and h of the pseudo-synaptic coupling converters 21 to 2N and the non-linear converter 2b are changed, and correction signals representing correction amounts △ W1 to Wn and △ h of the conversion parameters from the correction unit are received. , Wi + △ Wi; i = 1, 2,..., Nh + △ h (Equation 2).

FIG. 4 is a conceptual diagram of a signal conversion means in which four neural elements are connected 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. 4, reference numerals 211 to 244 denote pseudo-synaptic coupling converters,
Reference numerals 201 to 204 denote add-nonlinear converters that combine the adder 2a and the non-linear converter 2b described in FIG. In FIG. 4, as in FIG. 3, the illustration is omitted because it becomes complicated, but the input lines receiving the correction signal from the correction means are connected to the pseudo-synaptic coupling converters 211 to 244 and the addition nonlinear converter 20.
1 to 204 are connected. 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. 3 is performed in parallel.

FIG. 5 is a block diagram showing the configuration 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. Reference numeral 32 denotes a correction unit that calculates a correction amount of the signal conversion unit 31 in the learning mode. Hereinafter, an operation when learning of the signal processing unit is performed will be described with reference to FIG. The signal conversion means 31 has N inputs S
_In (X), M outputs S _out (X) are output.
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 nerve elements according to the learning mode described above.

FIG. 6 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. Signal processing means 3
4X, the signal conversion means 31X receives the input S
_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. Thereafter, the same processing is performed in the signal processing units 34Y and 34Z, and the final output S
_hout (Z) (h = 1 to M) is output. Final output S
_hout (Z) is also sent to the error calculating means 33. In the error calculating means 33, the square error evaluation function COST
Based on (Equation 4), the ideal output T (T1...
, TM) is calculated, and the error signal δ _h (Z) is sent to the correction means 32Z.

[0018]

(Equation 1)

Here, 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 unit 32Z calculates the correction amount △ W (Z) of the conversion parameter of the signal conversion unit 31Z according to the procedure described above, calculates the error signal to be sent to the correction unit 32Y based on (Equation 6), and calculates the correction signal. ΔW (Z) is sent to the signal conversion means 31Z, and the error signal δ (Y) is
Send to Y The signal conversion means 31Z outputs the correction signal △ W (Z)
Modify internal parameters based on. Note that the error signal δ (Y) is given by (Equation 6).

[0020]

(Equation 2)

Here, W _ij (Z) is the signal conversion means 31
Z is a conversion parameter of the pseudo-synaptic coupling converter of Z.
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, outputs 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 speeding up learning, which is known as an inertia term, are omitted for simplicity of description. It is not binding on the invention.

In this manner, the neural network model means can provide the environmental data (the initial temperature in the heating chamber, the initial temperature of the food, the commercial power supply voltage level, the type of the food, etc.) and the atmospheric temperature data in the heating chamber when cooking. By learning the relationship with the temperature (front surface, center, back surface temperature) data of the food, it is possible to express in a natural manner a control method that is not easily described by simple rules. In the present embodiment, the temperature estimating means 11 is configured by incorporating the information thus obtained. Specifically, the temperature estimating means 11 is configured using only the signal converting means 31X, 31Y, and 31Z of the multilayer perceptron after sufficiently learning as the neural network schematic means. The data actually learned will be described.

FIG. 7 shows the characteristics when cooking is performed when the initial temperature of the heating chamber 1 is low, the commercial power supply voltage is 100 V, the type of food is one horse mackerel, and the initial temperature of the food is about 10 ° C. It is a thing. FIG. 7A shows a change in the environmental physical quantity detection means 5 (atmospheric temperature in the heating chamber 1), FIG. 7B shows the surface temperature of the food, FIG. 7C shows the center temperature of the food, and FIG. 7
(D) shows a change in the back surface temperature of the food. The temperature of the food is measured by a thermocouple or the like. FIG.
Means that the initial temperature of the heating chamber 1 is high and the commercial power supply voltage is 100
V, the type of food is one horse mackerel, the initial temperature of the food is about 10
It shows the characteristics when cooking at a temperature of ° C.
FIG. 9 shows that the initial temperature of the heating chamber 1 is low and the commercial voltage 110
V, the type of food is one horse mackerel, the initial temperature of the food is about 10
It shows the characteristics when cooking at a temperature of ° C.
FIG. 10 shows that the initial temperature of the heating chamber 1 is high,
10 V, the type of food is one horsetail, and the initial temperature of the food is about 10 ° C., showing the characteristics when cooking.

FIGS. 8 (a) to 8 (d), FIGS. 9 (a) to 9 (d), and FIGS. 10 (a) to 10 (d) show FIG. 7 (a).
7 to FIG. 7D. It can be seen that the output voltage change (atmospheric temperature in the heating chamber 1) of the environmental physical quantity detection means 5 varies depending on the initial temperature in the heating chamber 1 and the amount of food. Similarly, when the power supply voltage is changed or the type of food is changed, the output changes differently. Such an experiment was similarly performed for all combinations of environments when cooking actually. Then, the experimental data was input to the neural network model means for learning. That is, the heating chamber 1 of the environmental physical quantity detecting means 5 is provided to the neural network schematic means.
Inside temperature information, atmosphere temperature information one minute before the present time as atmosphere temperature gradient information, commercial power supply voltage level information of the voltage level detecting means 6, and elapsed time information from power-on obtained by the timer means 7. 5 information of the category information obtained from the category selection key 9 and 3 information of the surface temperature information, the center temperature information, and the back surface temperature information of the food as the ideal output are input and learned. Establish signal conversion means 31X, 31Y, 31Z, and use them as surface temperature estimating means 11
Incorporated in.

Next, the operation will be described with reference to the system configuration diagram shown in FIG. First, the cooked food is put into the heating chamber 1 and the cooking category is selected by the category selection key 9 of the operation means 8. Then, cooking is started by the cooking key 10. The category information is input to the temperature estimating unit 11 via the control unit 12. The control means 12 outputs a signal to start time measurement to the time measurement means 7 and outputs a heating start signal to cause the heating supply means 2 to generate heat. The timing information of the timing means 7 is input to the temperature estimating means 11. The environmental physical quantity information (atmospheric temperature information) in the heating chamber 1 is digitally converted by the A / D converter 14 from the output of the environmental physical quantity detector 5, and the temperature estimating means 11 is moment by moment.
Is being entered. The voltage level information of the commercial power supply voltage from the voltage level detecting means 6 is digitally converted by the A / D converting means 15 and input to the temperature estimating means 11. The temperature estimating means 11 estimates the surface temperature, the center temperature, and the back surface temperature of the food every moment based on the input signals and information, and outputs the information to the control means 12. The control means 12 controls the heating supply means 2 based on the estimated temperature information.

Further, the clocking means 7, the temperature estimating means 11 and the control means 12 are all constituted by 4-bit microcomputers, but they can be constituted by one microcomputer as a matter of course. The temperature estimating means 11 includes temperature gradient information of the environmental physical quantity detecting means 5 (two pieces of information of the current time and one minute before), voltage level information of the commercial power supply voltage obtained from the voltage level detecting means 6, Although five pieces of information, that is, information about the elapsed time from the time of turning on the power and the category information of the food obtained from the category selection key 9 are input, this limitation does not restrict the present invention. Although the ambient temperature information is used as the environmental physical quantity information, it goes without saying that smoke information, burnt color information, humidity information, steam information, etc., or a combination thereof can also be applied. Further, in this embodiment, the electric roaster is used as the cooking utensil. However, a microwave oven, a gas oven, or the like may be used. That is, since the temperature of the cooked food can be estimated, the cooking may be terminated in the vicinity where the phase transition from solid (ice) to liquid (water) occurs (around 0 ° C.). As environmental physical quantity information at this time,
The ambient temperature may be used, and information on the amount of electric wave absorption (electric field intensity) of the cooked food is optimal.

As described above, according to the present embodiment, temperature estimation incorporating neural network model means having a plurality of fixed connection weighting factors of a neural network already learned in an environment of a heating room where cooking is actually performed. Since it has a configuration with means,
The completed state of the cooked food can be detected based on the front surface temperature, the center temperature, and the back surface temperature, and the cooking state can be improved as compared with the conventional example, and optimal automatic cooking can be realized.

[0028]

As is apparent from the above embodiments, according to the present invention, a heating chamber for storing foods for cooking,
Heating supply means for heating the food, environmental physical quantity detecting means for detecting an environment in the heating chamber, voltage level detecting means for detecting a voltage level of a commercial power supply voltage, output of the environmental physical quantity detecting means, and the voltage Temperature estimating means for estimating the temperature of the food based on the output of the level detecting means,
And control means for controlling the heating supply means based on the output of the temperature estimating means, so that the actual temperature of the cooked food capable of recognizing the finished state of the cooked food can be indirectly detected, such as the conventionally performed atmosphere temperature. The finished cooking state can be improved as compared with the case where the optimum cooking time is determined from the gradient of.

Further, since the temperature estimating means estimates at least one of the surface temperature, the center temperature, and the back surface temperature of the food, the overall finished state from the front surface to the inside of the food can be known. .

Further, the temperature estimating means is a fixed nerve for estimating the temperature (surface temperature, center temperature, back surface temperature) of the food obtained by a method simulating a neural network composed of a plurality of neural elements. Equipped with a neural network model having a plurality of connection weighting factors inside the network, or provided with a hierarchical neural network model which is constructed by combining layers composed of a plurality of neural elements in multiple layers. In addition, the temperature of the food can be estimated regardless of the initial temperature in the heating chamber, the initial temperature of the food, the amount of the food, and the like, and automatic cooking can be performed.

Further, the environmental physical quantity detecting means includes temperature detecting means for detecting the ambient temperature in the heating chamber, and the temperature detecting means indirectly estimates the temperature of the food, so that the temperature of the food is actually detected. There is no need to use a sensor (for example, a pyroelectric infrared sensor that measures the surface temperature of the food in a non-contact manner, or a temperature sensor that directly contacts the food), thereby reducing costs.

[Brief description of the drawings]

FIG. 1 is a system configuration diagram of a cooking utensil according to an embodiment of the present invention.

FIG. 2 is a front view of an operation unit and a display unit of the cooking appliance.

FIG. 3 is a conceptual diagram of a neural element which is a constituent unit of a neural network schematic means used in the cooking appliance.

FIG. 4 is a conceptual diagram of a signal conversion signal means constituted by neural elements used in the cooking appliance.

FIG. 5 is a block diagram of a signal processing unit employing an error back propagation method as a learning algorithm used in the cooking appliance.

FIG. 6 is a block diagram showing a configuration of a multilayer perceptron using a neural network model used in the cooking appliance.

FIGS. 7A to 7D show examples of experimental data of the cooking utensil.

8 (a) to 8 (d) are diagrams showing other examples of experimental data of the cooking utensil.

9 (a) to 9 (d) are views showing other examples of experimental data of the cooking utensil.

10 (a) to 10 (d) are diagrams showing other examples of experimental data of the cooking utensil.

FIG. 11 is a system configuration diagram of a conventional cooking appliance.

FIGS. 12A and 12D are diagrams showing examples of experimental data of the cooking utensil.

FIG. 13 is a view showing another example of the experimental data of the cooking appliance.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Heating room 2 Heat supply means 5 Environmental physical quantity detection means 6 Voltage level detection means 11 Temperature estimation means 12 Control means

 ──────────────────────────────────────────────────続 き Continued on the front page (72) Kenji Watanabe 1006 Kazuma Kadoma, Kadoma, Osaka Prefecture Inside Matsushita Electric Industrial Co., Ltd. No. Matsushita Giken Co., Ltd. (56) References JP-A-3-5622 (JP, A) JP-A-57-125412 (JP, A) JP-A-62-5589 (JP, A) JP-A-62-268082 (JP, A) JP-A-2-93708 (JP, A) JP-A-2-292602 (JP, A) JP-A-3-48301 (JP, A) JP-A-3-118606 (JP, A) JP-A-3-132415 (JP, A) JP-A-3-154101 (JP, A) JP-A-61-161615 (JP, U) JP-B-51-35909 (JP, B2)

Claims (2)

(57) [Claims] 1. A heating chamber for storing cooked food for cooking, heating supply means for heating the cooked food, environmental physical quantity detection means for detecting an environmental physical quantity such as an atmospheric temperature in the heating chamber, and a commercial power supply A voltage level detecting means for detecting a voltage level of the voltage; a surface temperature of the cooking object based on outputs of the environmental physical quantity detecting means and the voltage level detecting means ;
At least one of the core temperature and the back surface temperature
Consists of a neural network model that estimates during cooking.
A cooking appliance comprising: a temperature estimating unit that is provided; and a control unit that controls the heating and supplying unit based on an output of the temperature estimating unit. 2. A temperature estimating means, comprising: a plurality of neural networks obtained by a method simulating a neural network composed of a plurality of neural elements .
2. The cooking appliance according to claim 1 , further comprising a neural network model having a connection weight coefficient therein .