JPH0556860A - Cooker - Google Patents

Abstract

PURPOSE:To indirectly detect the cooking completion time of food for improving the finish state of cooking by a method wherein the temperatures of food under cooking are estimated in real time through the environmental physical quantity in a heating compartment that can actually be measured and detected. CONSTITUTION:An environmental physical quantity detector 5 senses the environment in a heating compartment 1 and detects the ambient temperature in the heating compartment 1. A voltage level detector 6 detects the voltage level of a commercial power source, and a timer 7 counts the elapsed time period since the power is turned on. An operating device 8 consists of a category selecting key 9 by which the category of food to be cooked is selected, and a cooking key 10 by which cooking is started and stopped. A temperature estimating device 11 estimates the surface, central and rear face temperature of food under cooking according to the outputs from the environmental physical quantity detector 5, voltage level detector 6, timer 7 and category selecting key 9, and a controller 12 controls heaters 2 according to the outputs from the temperature estimating device 11. Thereby, the actual temperature of food under cooking detected, so that the finish state of cooking is improved.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a cooking utensil for automatically cooking in a microwave oven, a gas oven, a roaster or the like.

[0002]

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

As shown in the figure, a heating chamber 1 is for cooking food by putting it in it. A heating supply means (heater) 2 for heating the food is provided and a temperature detecting means such as a thermistor for detecting the internal temperature. 3 is provided. The control means 4 controls the heating supply means 2 based on the 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 and the initial temperature. Therefore, the output voltage gradient of the temperature detecting means 3 is measured for several minutes after the power is turned on, and if the gradient is steep, it is determined that the weight of the food is light, and if the gradient is gentle, the weight is heavy, and the voltage gradient is determined. The optimum cooking time was determined by multiplying the constant K by The output voltage characteristic of the temperature detecting means 3 is shown in FIG. Figure 1
2 (a) has a light weight, and FIG. 12 (b) has a heavy weight. And I did a lot of cooking experiments and decided the constant.

[0004]

In such a conventional cooking utensil (here, a roaster), the gradient of the ambient temperature in the heating chamber 1 is detected, and the cooking product (for example, fish grill) is detected based on the gradient. The cooking time was decided by judging the weight of), so 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 finishing the cooking, the initial temperature in the heating chamber 1 becomes very high. In this case, when a heavy food is cooked, the output voltage characteristic of the temperature detecting means 3 becomes as shown in FIG. 13, and the temperature in the heating chamber 1 drops for a moment. This is because even if cooking is started, the temperature in the heating chamber 1 is high, and thus the heat in the heating chamber 1 is absorbed by the food. In such a case, it was difficult to determine the optimum cooking time by the above method. Also, the heating supply means 2
Since is composed of a heater, fluctuations in commercial power supply voltage will have a considerable effect on the completion of cooking. That is,
Depending on the environment when starting cooking (type of food, initial temperature of heating chamber 1, initial temperature of food, power supply voltage, etc.),
There was a problem that the quality of cooking was considerably varied. Further, in terms of the burning of fish and the like, the baked state of the surface is also important, but the temperature rise inside the fish is said to be best at 60 to 70 ° C. In consideration of this point, there is a problem that it is very difficult to detect the completion of cooking from the aspect of surface burning and temperature rise inside.

The present invention solves the above problems by estimating the temperature of cooked food in real time with the physical quantity of the environment in the heating chamber, which can actually measure and detect the cooked food, and indirectly detects the completion of the cooked food. The purpose is to improve the finished state.

[0006]

In order to achieve the above-mentioned object, the present invention provides a heating chamber for storing food to be cooked, a heating supply means for heating the food, and an environment in the heating chamber. Environmental physical quantity detecting means for detecting, voltage level detecting means for detecting the power supply level of the commercial power supply voltage, surface temperature of the cooking based on the output of the environmental physical quantity detecting means and the output of the voltage level detecting means, the central temperature, The temperature estimating unit includes a temperature estimating unit that estimates at least one of the back surface temperatures and a control unit that controls the heating supply unit based on the output of the temperature estimating unit. The temperature estimating unit includes a plurality of neural elements. Equipped with a hierarchical neural network model means that internally has a plurality of coupling weight coefficients of a fixed neural network for estimating the temperature of a cooked food obtained by a method simulating the neural network described above. It is a means for solving the problems that.

[0007]

According to the present invention, by the above-mentioned means for solving the problems, the cooking is actually performed by 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 temperature estimation means having the neural network model means for learning all the actual cooking environment and having the fixed connection weight coefficient inside has at least one of the surface temperature, the central temperature and the back surface temperature of the cooked food. One is estimated every moment. The control means controls the heating supply means (heater in the electric roaster) on the basis of the output of the temperature estimation means, and indirectly detects the temperature rise of the food, thereby recognizing the optimum completion of cooking.

[0008]

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

As shown in the figure, the environmental physical quantity detection means 5
Is for detecting the environment inside the heating chamber 1, and in the present embodiment, is constituted by a thermistor or the like to detect the ambient temperature inside the heating chamber 1. The voltage level detecting means 6 detects the voltage level of the commercial power supply voltage. The time counting means 7 counts the time elapsed since the power was turned on. The operation means 8 is a category selection key 9 for selecting a category of food and cooking start /
It is composed of a cooking key 10 for stopping. The temperature estimation means 11 estimates the surface temperature, center temperature, and back surface temperature of the cooked food based on the outputs of the environmental physical quantity detection means 5, the voltage level detection means 6, the timing means 7, and the category selection key 9, and the control means. Reference numeral 12 controls the heating supply means 2 based on the output of the temperature estimation means 11. The display means 13 comprises a fluorescent display tube and displays the remaining cooking time and the like.
Further, the A / D conversion means 14 and 15 respectively convert the outputs of the environmental physical quantity detection means 5 and the voltage level detection means 6 into digital quantities. Operation means 8 and display means 1
3 is configured as shown in FIG.

Since the solving means used in the conventional control method cannot be applied to the means constituting the temperature estimating means 11, it is constructed by a method simulating an optimal neural network as a multidimensional information processing method. There is. In the method that imitates the neural network, a method of using a plurality of connection weight coefficients of the neural network for estimating the temperature (surface temperature, central temperature, back surface temperature) of the cooked food as a fixed table and leaving the learning function There are ways to adapt to the environment and users. The present embodiment is provided with a temperature estimating means 11 having a neural network model means having therein a fixed coupling weighting coefficient for estimating the temperature of a food item obtained by a method simulating a neural network.

Factors that affect the completion of the cooked food include the initial temperature in the heating chamber, the initial temperature of the cooked food, the type (category) of the cooked food, and the voltage level of the commercial power supply. Due to these factors, the completion of the cooked food will vary greatly.

The fixed weighting coefficient fixed in the neural network for estimating the temperature of the cooked food is the surface temperature of the cooked food when cooked in the actual cooking environment (environment in which various factors described above are combined). Collect data on how the center temperature, backside temperature, and ambient temperature in the heating chamber change, and correlate environmental data, ambient temperature data in the heating chamber, and cooking product temperature (front, center, backside) data. It can be obtained by learning the neural network model means. As a neural network schematic means to be used, reference 1 (DE Ramelhart et al., 2 authors, "PDP model" translated by Shunichi Amari, 1989), reference 2 (Kaoru Nakano, 7 authors, "Basics of Neurocomputer") Published by Corona, P102, 1990), JP-B-63-55106
Some of them are disclosed in the official gazette. 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. 3 is a conceptual diagram of a neural element which is a constituent unit of the neural network model means. In FIG. 3, 21 to 2
N is a pseudo synapse coupling converter simulating synaptic coupling of nerves, and 2a is pseudo synapse coupling converters 21 to 2N.
2b is a set nonlinear function, for example, a sigmoid function whose threshold is 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 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 means. 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 to X
It receives n 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 by the correction signal indicating the correction amounts ΔW1 to Wn and Δh of the conversion parameters from the correction means. , Wi + ΔWi; i = 1, 2, ..., N h + Δh (formula 2).

FIG. 4 is a conceptual diagram of a signal converting means constructed by connecting four neural elements in parallel. Needless to say, the following description does not specify that the number of neural elements constituting this signal converting means is four. In FIG. 4, 211 to 244 are pseudo synapse coupling converters,
201 to 204 are addition nonlinear converters that combine the adder 2a and the nonlinear converter 2b described in FIG. 4, the illustration is omitted because the drawing is complicated as in FIG. 3, but the input lines for receiving the correction signal from the correction means are pseudo synapse coupling converters 211 to 244 and an addition nonlinear converter 20.
It is connected to 1-204. The pseudo synapse coupling converters 211 to 244 also serve as coupling weight coefficients. Regarding 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 back-propagation method is adopted as the learning algorithm, and 31 is the above-mentioned signal converting means. However,
Here, M neural elements for receiving 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 when learning the signal processing means will be described with reference to FIG. The signal converting means 31 has N inputs S
Upon receiving _in (X), it outputs M outputs S _out (X).
The correction means 32 includes an input signal S _in (X) and an output signal S _out.
Upon receiving (X), the process waits until M error signals δ _i (X) are input from the error calculating means or the signal converting means in the subsequent stage. The error signal δ _i (X) is input and the correction amount is ΔW _ij = δ _i (X) · S _iout (X) · (1−S _iout (X)) · S _jin (X) (i = 1 to N , J = 1 to M) (formula 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. 6 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 32X, 32Y, and 3 are provided.
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. Signal processing means 3
In 4X, the signal conversion means 31X receives the input S
_{Receives iin} (X) (i = 1 to N) and outputs S _jout (X)
(J = 1 to K) is output. The correction means 32X uses the signal S
_The error signal δ is received by receiving _iin (X) and the signal S _jout (X).
Wait until _j (X) (j = 1 to K) is input. Thereafter, the same processing is performed in the signal processing means 34Y and 34Z, and the final output S from the signal converting means 31Z.
_hout (Z) (h = 1 to M) is output. Final output S
_hout (Z) is also sent to the error calculation means 33. In the error calculating means 33, the square error evaluation function COST
An ideal output T (T1 ...
, TM) is calculated, and the miscalculated signal δ _h (Z) is sent to the correction means 32Z.

[0018]

[Equation 1]

However, η is a parameter that determines the learning speed of the multilayer perceptron. Next, when the evaluation function is a squared error, the error signal is δh (Z) = − η · (S _hout (Z) −T _h ) (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 outputs the correction 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).

[0020]

[Equation 2]

Where: W _ij (Z) is the signal conversion means 31
It is a conversion parameter of the pseudo synapse coupling converter of Z.
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 uses the environmental data (the initial temperature in the heating chamber, the initial temperature of the cooking product, the commercial power supply voltage level, the type of the cooking product, etc.) when cooking and the ambient temperature data in the heating chamber. 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 way the control method that is not easy to describe with simple rules. In this embodiment, the temperature estimation means 11 is configured by incorporating the information thus obtained. Specifically, the temperature estimating means 11 is configured by using only the signal converting means 31X, 31Y, 31Z of the multilayer perceptron after sufficiently learning as the neural network schematic means. The data actually learned will be described.

FIG. 7 shows 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 eel, and the initial temperature of the food is about 10 ° C. It is a thing. FIG. 7A shows changes in the environmental physical quantity detection means 5 (ambient temperature in the heating chamber 1), FIG. 7B shows the surface temperature of the cooked food, FIG. 7C shows the central temperature of the cooked food, and FIG. 7
(D) has shown the change of the back surface temperature of a cooked material. The temperature of the cooked food is measured with a thermocouple or the like. Figure 8
Indicates that the initial temperature of the heating chamber 1 is high and the commercial power supply voltage is 100
V, the type of cooked food is 1 shark, the initial temperature of cooked food is about 10
It shows the characteristics when cooked at ℃.
In FIG. 9, the initial temperature of the heating chamber 1 is low and the commercial voltage 110
V, the type of cooked food is 1 shark, the initial temperature of cooked food is about 10
It shows the characteristics when cooked at ℃.
FIG. 10 shows that the heating chamber 1 has a high initial temperature and the commercial power supply voltage 1
10V, the type of cooked food is one shark, and the initial temperature of the cooked food is about 10 ° C., which shows the characteristics when cooking.

FIGS. 8 (a) to 8 (d), 9 (a) to 9 (d), and 10 (a) to 10 (d) are shown in FIG. 7 (a).
~ Corresponds to Fig. 7 (d). It can be seen that the output voltage change (ambient 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 when the type of food is changed, the output changes differently. Such an experiment was carried out in the same manner for all combinations of actual cooking environments. 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 detection means 5 is connected to the neural network schematic means.
Atmosphere temperature information inside, atmosphere temperature information 1 minute before the present time as atmosphere temperature gradient information, commercial power supply voltage level information of the voltage level detection means 6, and elapsed time information from the time of power-on obtained from the timekeeping means 7. And 5 pieces of category information obtained from the category selection key 9 and 3 pieces of information of the surface temperature, the central temperature information and the back surface temperature of the cooking product as ideal outputs are input and learned, and the neural network model means The signal temperature conversion means 31X, 31Y and 31Z are established, and the surface temperature estimation means 11 is used as the neural network schematic means.
Is incorporated into.

Next, the operation will be described based on the system configuration diagram shown in FIG. First, the food to be cooked is placed in 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 with the cooking key 10. The category information is input to the temperature estimation means 11 via the control means 12. The control means 12 outputs a signal to start timing to the timing means 7 and also 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 estimation means 11. Then, the environmental physical quantity information (atmosphere temperature information) in the heating chamber 1 is digitally converted from the output of the environmental physical quantity detection means 5 by the A / D conversion means 14, and the temperature estimation means 11 is added every moment.
Are typing in. Further, 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 item on the basis of these input signals and information momentarily, and outputs the information to the control means 12. The control means 12 controls the heating supply means 2 based on this estimated temperature information.

Further, the clocking means 7, the temperature estimating means 11, and the control means 12 are all constructed by a 4-bit microcomputer, but it is of course possible to construct them by one microcomputer. It should be noted that the temperature estimation means 11 includes temperature gradient information of the environmental physical quantity detection means 5 (two pieces of information at the present time and one minute ago), voltage level information of the commercial power supply voltage obtained from the voltage level detection means 6, and the time counting means 7. Five pieces of information, namely, elapsed time information after turning on the power source and category information of the cooking product obtained from the category selection key 9 are input, but this limitation does not restrict the present invention. Further, although the ambient temperature information is used as the environmental physical quantity information, it goes without saying that smoke information, burn color information, humidity information, steam information, or a combination thereof can also be applied. Further, in the present embodiment, the electric roaster is used as the cooking utensil, but a microwave oven, a gas oven or the like may be used. Particularly, in the microwave oven, it is most suitable when the food is thawed. In other words, since the temperature of the cooked product can be estimated, it suffices to finish the cooking in the vicinity of the phase transition from solid (ice) to liquid (water) (around 0 ° C.). As the environmental physical quantity information at this time,
The ambient temperature may be sufficient, and the information on the absorption amount of electric waves (electric field strength) of the food is optimal.

As described above, according to the present embodiment, the temperature estimation incorporating the neural network model means having a plurality of fixed coupling weight coefficients of the neural network already learned in the environment of the heating chamber where the cooking is actually performed Since it is configured with means,
The finished state of the cooked food can be detected by the surface temperature, the center temperature, and the backside temperature, and the cooked 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, there is provided a heating chamber for storing food to be cooked,
Heating supply means for heating the food, environmental physical quantity detection means for detecting the environment in the heating chamber, voltage level detection means for detecting the voltage level of the commercial power supply voltage, output of the environmental physical quantity detection means and the voltage Temperature estimation means for estimating the temperature of the cooking product based on the output of the level detection means,
Since the control means for controlling the heating and supplying means based on the output of the temperature estimating means is provided, the actual temperature of the cooked food capable of recognizing the finished state of the cooked food can be indirectly detected, and the ambient temperature conventionally used, etc. Compared to the one in which the optimum cooking time was determined from the gradient of, the finished state of cooking can be improved.

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

Further, the temperature estimation means is a fixed nerve for estimating the temperature (surface temperature, central temperature, back surface temperature) of the food obtained by a method simulating a neural network composed of a plurality of neural elements. Since the neural network model means having a plurality of coupling weight coefficients of the network therein is provided, or the hierarchical neural network model means constructed by combining layers composed of a plurality of neural elements in multiple layers is provided. The temperature of the cooked food can be estimated regardless of the initial temperature in the heating chamber, the initial temperature of the cooked food, the amount of the cooked food, etc., and automatic cooking is possible.

Further, the environmental physical quantity detection means is provided with a temperature detection means for detecting the ambient temperature in the heating chamber, and the temperature detection means indirectly estimates the temperature of the cooked food, so that the temperature of the cooked food is actually detected. It is not necessary to use a sensor (for example, a pyroelectric infrared sensor that measures the surface temperature of a food product in a non-contact manner or a temperature sensor that directly contacts the food product), and the cost can be reduced.

[Brief description of drawings]

FIG. 1 is a system configuration diagram of a cookware 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 utensil.

FIG. 3 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. 4 is a conceptual diagram of a signal conversion signal means composed of neural elements used in the cooking utensil.

FIG. 5 is a block diagram of a signal processing unit that employs an error backpropagation method as a learning algorithm used for the cooking utensil.

FIG. 6 is a block diagram showing a configuration of a multilayer perceptron using a neural network schematic means used in the cooking utensil.

7A to 7D are diagrams showing an example of experimental data of the cooking utensil.

8A to 8D are views showing another example of the experimental data of the cooking utensil.

9A to 9D are diagrams showing another example of experimental data of the cooking utensil.

FIG. 10 (a) to (d) are views showing another example of experimental data of the cooking utensil.

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

FIG. 12 (a) and (d) are views showing an example of experimental data of the cooking appliance.

FIG. 13 is a diagram showing another example of experimental data of the cooking utensil.

[Explanation of symbols]

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

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Nakason Son 10-1 Higashisanda, Tama-ku, Kawasaki-shi, Kanagawa Matsushita Giken Co., Ltd.

Claims (5)

[Claims] 1. A heating chamber for storing food for cooking, a heating supply means for heating the food, an environmental physical quantity detection means for detecting an environment in the heating chamber, and a power supply level of a commercial power supply voltage. Voltage level detecting means for detecting, temperature estimating means for estimating the temperature of the cooking product based on the outputs of the environmental physical quantity detecting means and the output of the voltage level detecting means, and the heating supplying means based on the output of the temperature estimating means. And a control means for controlling the cooking utensil. 2. The cooking utensil according to claim 1, wherein the temperature estimating means estimates at least one of a surface temperature, a center temperature and a back surface temperature of the food. 3. The temperature estimating means estimates a plurality of coupling weight coefficients of a fixed neural network for estimating the temperature of a food item obtained by a method simulating a neural network composed of a plurality of neural elements. The cooking utensil according to claim 1, further comprising a neural network model means provided therein. 4. The cooking utensil according to claim 1, wherein the temperature estimation means comprises a hierarchical neural network schematic means constructed by combining layers composed of a plurality of neural elements in multiple layers. 5. The cooking utensil according to claim 1, wherein the environmental physical quantity detecting means includes a temperature detecting means for detecting an ambient temperature in the heating chamber.