EP0928929B1 - Automatisches Kochgerät mit einem neuronalen Netzwerk - Google Patents

Automatisches Kochgerät mit einem neuronalen Netzwerk Download PDF

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Publication number
EP0928929B1
EP0928929B1 EP19990400041 EP99400041A EP0928929B1 EP 0928929 B1 EP0928929 B1 EP 0928929B1 EP 19990400041 EP19990400041 EP 19990400041 EP 99400041 A EP99400041 A EP 99400041A EP 0928929 B1 EP0928929 B1 EP 0928929B1
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EP
European Patent Office
Prior art keywords
cooking
cavity
neurones
temperature
dish
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Lifetime
Application number
EP19990400041
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English (en)
French (fr)
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EP0928929A1 (de
Inventor
Jean-Paul Chevrier
Pascal Oudart
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Europeenne De Fabrication D'enceintes Mi Cie
Brandt Cooking SAC
Brandt Industries SAS
Original Assignee
Compagnie Europeenne pour lEquipement Menager SA
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Publication of EP0928929A1 publication Critical patent/EP0928929A1/de
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Publication of EP0928929B1 publication Critical patent/EP0928929B1/de
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24C—DOMESTIC STOVES OR RANGES ; DETAILS OF DOMESTIC STOVES OR RANGES, OF GENERAL APPLICATION
    • F24C7/00—Stoves or ranges heated by electric energy
    • F24C7/08—Arrangement or mounting of control or safety devices
    • F24C7/087—Arrangement or mounting of control or safety devices of electric circuits regulating heat

Definitions

  • the invention relates to the field of cooking devices automatic for oven.
  • the invention applies preferably to ovens traditional.
  • the purpose of automatic cooking devices is to simplify maximum life of the user while guaranteeing the best result cooking possible.
  • the object of the invention is to propose a device based on the use of a neural network, which does not require the establishment of a system of empirical rules that are otherwise laborious to design and unsuitable for the cooking operation.
  • the device according to the invention does not use preferably two physical measurements, the temperature prevailing in the cavity and moisture emitted by the food during cooking, which does not requires no complex weighing devices in the oven. It's a very automated device because the only information required of the user is the family of dishes, no other information is needed for a good cooking process, which requires little effort on the part of the user.
  • the dish family corresponds to the nature of the food, for example "chicken" or "pie”.
  • an automatic cooking device having an oven, at least one temperature sensor measuring the temperature in the oven cavity, at least one humidity sensor measuring moisture in the oven cavity, and a network of neurons, selection means to which the user provides a given family of dishes information, characterized in that that the device also comprises means to associate with the neural network a set of link weights between the neurons, the game being adapted to the dish family, means of which launch a cooking method adapted to the dish family, extraction means which extract a group of parameters from the measurements made by the temperature sensor and the sensor of moisture, in that the neural network estimates a cooking time remaining from the parameter group, and that the cooking means perform the remaining cooking time in an open loop.
  • FIG. 1 schematically shows a cooking device automatic according to the invention.
  • the arrows represent exchanges of data between the different parts of the device, the letters or groups of letters near the arrows symbolically represent the data transmitted.
  • This device comprises a cavity 10 of oven, at least one temperature sensor 11, at least one humidity sensor 12, means 13 cooking.
  • the plate 14 is introduced into the cavity 10 to be cooked.
  • the temperature sensor 11 makes it possible to measure the temperature T in the cavity 10
  • the humidity sensor makes it possible to measure the humidity H in the cavity 10.
  • the sensors 11 and 12 can be located in the cavity 10, but it is not obligatory.
  • the temperature sensor 11 is located in cavity 10, while the two moisture sensors are located in an air guide shown in Figure 2 and connecting the cavity 10 to external environment and allowing ventilation of the cavity 10.
  • the device can have other types of sensors, but these are sufficient.
  • the device also comprises means 13 for cooking, which usually comprise heating elements not shown on the Fig.
  • heating elements can be elements heated by the ground, that is to say by the underside of the cavity 10, or by the grill, that is to say by the upper face of the cavity 10, or heating elements arranged around a ventilation system of the cavity 10, or a combination of the above heating elements.
  • the 13 cooking means can heat the cavity 10 and cook the dish 14 via heating elements.
  • the attachments of the elements 11, 12 and 13 to the cavity 10 are represented by dashed lines.
  • the device also comprises means 20 for extracting parameters which develop from the measurements made by the temperature sensors 11 and 12 humidity a GP group of parameters which will be detailed later.
  • the device also comprises a network 30 of neurons which receives in input group GP of previous parameters and which outputs to cooking means 13 a cooking time TCR remaining
  • the device also includes selection means 40 by which the user 50 provides the device FP family information flat, that is to say that the user indicates the category of food to which the dish belongs intended for cooking.
  • the selection means 40 may for example consist of a keyboard or a set of buttons, each button corresponding to a family of dishes. Dish families can by example be "pie”, “chicken”, “soup”, or other families define according to the particular application envisaged.
  • the families of FP dishes are preferably chosen so that the dishes of the same family have relatively homogeneous properties in cooking.
  • the plate 14 is introduced into the cavity 10.
  • the FP family information of dishes is provided to the means 40 for selection by the user 50.
  • the user triggers the cooking operation, pressing example on an on / off button.
  • FP information is transmitted by means 40 for selecting the cooking means 13.
  • the means 13 of start a cooking mode adapted to the FP family of dishes, by example for a family of dishes "pie" heating will be done especially by the floor heating element.
  • the FP information is transmitted to the network 30 neurons by the selection means 40.
  • the neuron network 30 is then configured in a manner adapted to the family of dishes FP, which is preferably homogeneous.
  • the neural network 30 has several successive layers of neurons 34, three layers 31 to 33 for example in Figure 1.
  • the neurons 34 of a given layer, for example the layer 32 are linked to the neurons 34 of the neighboring layers, the layers 31 and 33, by links 35.
  • each of these links is associated with a weighting, i.e. coefficient, all of these weights constituting a set of weights.
  • To each family of dishes FP corresponds a game of weights.
  • a weighting game adapted to the FP family of dishes is associated with the neural network 30 which is therefore configured in a manner suitable for the FP family of dishes.
  • the FP information will also be provided to the means 20 for extracting parameters which will also be configured appropriately for the FP family of dishes.
  • the two sensors 11 and 12 respectively go take measurements of temperature and humidity. Preferably, measurements are made from the beginning of cooking. From these measurements, the extraction means 20 will extract after a certain time which depends preferably on the family of flat FP a group of parameters GP which will be detailed later.
  • the parameter group GP is injected into input, that is to say on the side of the layer 31, the network 30 of neurons.
  • the neuron network 30 estimates a cooking time TCR remaining from the GP parameter group.
  • the TCR time is transmitted to the cooking means 13 which perform in loop open the remaining cooking time TCR.
  • Open loop means that from the moment when the time TCR has been transmitted to the cooking means 13, these are disconnected from the network 30 of neurons, perform the cooking time TCR remaining without any change in mode or cooking time is provided by the network 30 of neurons until the end of cooking.
  • the various means mentioned above are functional representations, and that the device may comprise a microprocessor responsible for carrying out all or part of the operations previously described as well as coordinate them.
  • Other links can conventionally exist between the various means of the device, as for example that between the temperature sensor 11 and the means cooking 13 allowing the cooking means 13 to control the cooking; they are not shown in Figure 1.
  • the group of parameters consists of the initial temperature Ti of the cavity 10, the derivative dT of the temperature of the cavity 10 with respect to the time, the flow rate. of water emitted by the dish at a first moment t 1 and the quantity Qe of water emitted by the dish from the beginning of cooking to a second time t 2 .
  • the initial temperature of the cavity 10 is the temperature in the cavity 10 at the beginning of cooking.
  • the derivative dT is the slope of the temperature over a certain period, it reflects the thermal mass of the plate 14, the thermal mass being the product mass (or weight) by calorisfique capacity; as inside the same family of dishes FP, the heat capacity is assumed to evolve similarly regardless of the flat 14, the derivative dT strongly depend on the weight of the flat 14. For example, in the family of chicken dish, from one chicken to another, the evolution of the dT derivative will depend mainly on the weight of the chicken.
  • the parameter of the derivative dT advantageously replaces the missing weight information of the plate 14:
  • the flow rate De corresponds to the humidity prevailing in the cavity at time t 1 .
  • the quantity Qe is the quantity of water emitted from the beginning of cooking to a time t 2 ; Qe therefore corresponds to the integration over time of the humidity prevailing in the cavity.
  • the instants t 1 and t 2 preferably depend on the FP plate family, they are optimized by performing tests.
  • the plate family FP is therefore data transmitted to the extraction means for extracting the parameter group GP.
  • these instants t 1 and t 2 coincide, which facilitates the operation of extracting the parameters.
  • the choice of these parameters results from a quality optimization of the estimation of the neuron network with respect to the complexity of the GP parameter group and the structure of the neuron network.
  • FIGs 2A and 2B show schematically respectively a top view and a side view of an air guide of an embodiment preferential of an automatic cooking device according to the invention.
  • the automatic cooking device uses an already existing air guide 1 for the ventilation of the oven cavity during cooking.
  • Air Guide 1 is located above the upper face of the cavity not shown on the Fig. Guide 1 allows ventilation of the cavity by evacuating air loaded with moisture from the water emitted by the dish during cooking.
  • the air leaving the cavity passes through an unrepresented inlet of the guide 1 before to arrive in the guide 1.
  • the arrows in solid lines represent the air circulation ambient and the triangles connected by dotted lines represent the flow of air Cooking.
  • the device comprises a first sensor 3 of humidity placed in zone 2 of guide 1.
  • the first sensor 3 is subjected to thermal stresses much less important than if it were placed in the cavity, which allows the use of a moisture sensor that does not support only slight thermal stresses.
  • this first sensor 3 measures the humidity contained in a mixture containing cooking air certainly, but also ambient air whose humidity is different from that of the cavity.
  • the device then comprises a second humidity sensor 6 placed in the zone 5 measuring only the ambient humidity, that is to say the external environment.
  • Zones 2 and 5 are represented symbolically, the outline exact of said areas being more complex, and the boundary between the said areas not being brutal.
  • the device includes advantageously two temperature sensors 4 and 7 respectively placed in the vicinity of the humidity sensors 3 and 6, close enough so that the temperature differences between where is placed a moisture sensor and the place where is placed the annex sensor of associated temperature are insignificant.
  • Sensor information Annexes 4 and 7 then allow the device to modulate the response of humidity sensors 3 and 6 depending on the temperatures at which the Humidity sensors 3 and 6 are subject.
  • the temperature sensor measuring the temperature of the cavity is advantageously placed in the cavity, it will also serve as a probe of temperature control for cooking.
  • the oven preferably has a ventilation system to homogenize the temperature in the cavity during cooking.
  • the neural network of Figure 1 will now be detailed in a preferred embodiment.
  • the numbers are those of Figure 1.
  • the 30 network of neurons comprises neurons 34 distributed in several layers 31 to 33.
  • the network has three layers of neurons, the layer 31, the intermediate layer 32, and the exit layer 33.
  • neurons 34 are interconnected by links 35.
  • the links 35 have weights.
  • the neurons of the input layer 31 correspond parameters of the GP parameter group, so they are preferably number of four.
  • the output layer 33 provides a single value, the time TCR cooking remaining, so it is preferably composed of a single neuron.
  • Each neuron in the middle layer 32 receives from each of the four neurons of the input layer 31 a signal.
  • the signal issued by a neuron na from the input layer 31 to a neuron nb of the layer 32 intermediate is the value of the parameter assigned to the neuron na weighted by the weighting of the existing link between na neurons and nb.
  • the neuron of layer 33 of output receives signals from the neurons of layer 32 intermediate, signals from which it emits a signal representing time TCR cooking remaining estimated.
  • each family of dishes FP corresponds a set of weightings of the links between neurons .
  • These games of Weights are determined during learning phases on examples with the help of a cook expert. The different examples as well by variations of dish, while remaining in the same family, as initial temperature variations, for example.
  • Each weighting game is advantageously determined in the following manner. On a series examples belonging to the same family of dishes, the comparison between the cooking time given by the expert cook and the estimated cooking time by the neural network, we then obtain a error. The expert cook estimates the cooking time on the basis of his sight, his sense of smell, his experience, etc.
  • the neural network is striving to correct, to minimize errors by a statistical method seeking the correlations between the different examples.
  • the set of weights obtained when errors are minimized will be the game retained for the dish family corresponding, for the cooking then carried out by the user.
  • the network is said to be convergent.
  • Each family of dishes having a specific weighting set, on a common neural network structure one can thus define several "virtual" neural networks, that is to say differ only in their game of weights, each "virtual" neural network being adapted and therefore optimized for a particular FP family of dishes. This allows to obtain a Neural network structure both simple and convergent.
  • Each family of dishes may require a number of more examples or less, depending on whether the family is "simple" or "complex".
  • a simple family of dishes is a family whose different elements have a almost identical behavior
  • a complex family of dishes is a family whose different elements have a more disparate behavior. For as the neural network converges, the number of neurons in the intermediate layer is sufficiently large.
  • the network will tend to "to specialize", ie to associate each neuron of the layer intermediate to one or two examples of learning, and during a cooking performed by the user, the neural network will estimate a time of cooking remains wrong if the dish and cooking conditions do not match not exactly one of the examples of the learning phase. It takes contrary force the neural network to "generalize" the examples of the learning phase by choosing a ratio between the number of examples and the number of neurons in the intermediate layer that is sufficiently great.
  • the intermediate layer comprises six neurons, and the number of examples per family of dishes during the learning phase is of the order of twenty.
  • the device according to the invention is simple and effective. It also the advantage of being scalable. Indeed, the introduction of a new family of dishes only requires storage of a new set of weights obtained during an additional learning phase on a few examples of dishes belonging to the new family.
  • the device according to the invention can also combine the means previously described for families of relatively complex dishes and other more traditional means, for example using relationships directly calculating the remaining cooking time from the measurements made by temperature and humidity sensors, for families very simple dishes, that is to say of which all the elements have a uniform behavior.

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Cookers (AREA)
  • Electric Ovens (AREA)

Claims (10)

  1. Automatische Kochvorrichtung mit einem Ofen, mindestens einem Temperatursensor (11), der die Temperatur (T) im Hohlraum (10) des Ofens mißt, mindestens einem Feuchtigkeitssensor (12), der die Feuchtigkeit (H) im Hohlraum (10) des Ofens mißt, einem Netzwerk (30) von Neuronen (34) und Auswahlmitteln (40), welchen der Benutzer (50) eine bestimmte Information für einen Typ (FP) von Gerichten liefert, dadurch gekennzeichnet, daß die Vorrichtung auch Mittel aufweist, um dem Netwerk (30) von Neuronen (34) einen Satz von Gewichtungen der Verbindungen (35) zwischen den Neuronen (34) zuzuordnen, wobei der Satz dem Gerichtentyp (FP) angepaßt ist, Garmittel (13), die einen Garmodus auslösen, der dem Gerichtentyp (FP) angepaßt ist, Extrahierungsmittel, die eine Gruppe (GP) von Parametern aufgrund der vom Temperaturensor (11) und vom Feuchtigkeitssensor (12) durchgeführten Messungen extrahieren, daß das Netwerk (30) von Neuronen (34) ausgehend von der Parametergruppe (GP) eine verbleibende Garzeit (TCR) schätzt, und daß die Garmittel (13) die verbleibende Garzeit (TCR) als offene Schleife ausführen.
  2. Vorrichtung nach Anspruch 1, dadurch gekennzeichnet, daß die Vorrichtung zwei Sensoren (3, 6) für die absolute Feuchtigkeit und eine Luftführung (1) mit einer ersten Zone (2), in der Luft aus dem Hohlraum und aus einem Außenbereich strömt, und mit einer zweiten Zone (5), in dem keine Luft aus dem Hohlraum, sondern lediglich Luft aus dem Außenbereich strömt, enthält, wobei der erste Feuchtigkeitssensor (3) in der ersten Zone (2) und der zweite Feuchtigkeitssensor (6) in der zweiten Zone (5) liegen.
  3. Vorrichtung nach Anspruch 2, dadurch gekennzeichnet, daß die Vorrichtung auch zwei Hilfs-Temperatursensoren (4, 7) enthält, wobei jeder Hilfs-Temperatursensor (4, 7) in der Nähe eines der Feuchtigkeitssensoren (3, 6) liegt, und daß die Vorrichtung die Antwort der Feuchtigkeitssensoren (3, 6) mit Hilfe der Hilfs-Temperatursensoren (4, 7) temperaturmoduliert.
  4. Vorrichtung nach einem der vorstehenden Ansprüche, dadurch gekennzeichnet, daß die Parametergruppe (GP) aus der Anfangstemperatur (Ti) des Hohlraums (10), der Ableitung (dT) der Temperatur des Hohlraums (10) in Bezug auf die Zeit, der vom Gericht (14) an einem ersten Zeitpunkt (t1) abgegebenen Wasseraustrag (De) und der vom Anfang des Garvorgangs bis zum zweiten Zeitpunkt (t2) vom Gericht (14) abgegebenen Wassermenge (Qe) besteht.
  5. Vorrichtung nach Anspruch 4, dadurch gekennzeichnet, daß der erste Zeitpunkt (T1) und der zweite Zeitpunkt (T2) sich überdecken.
  6. Vorrichtung nach einem der Ansprüche 4 und 5, dadurch gekennzeichnet, daß der erste und der zweite Zeitpunkt (t1, t2) den Gerichtentypen (FP) angepaßt ist.
  7. Vorrichtung nach einem der vorstehenden Ansprüche, dadurch gekennzeichnet, daß das Netzwerk (30) von Neuronen (34) aus drei aufeinanderfolgenden Schichten (31, 32, 33) besteht, nämlich aus der von vier Neuronen gebildeten Eingangsschicht (31), der Zwischenschicht (33) und der von einem Neuron gebildeten Ausgangsschicht (33).
  8. Vorrichtung nach Anspruch 7, dadurch gekennzeichnet, daß, da jeder Satz der Gewichtungen der Verbindungen (35) während einer Übung mit einem vorgegebenen Anzahl von Beispielen bestimmt ist, das Verhältnis zwischen der Anzahl von Beispielen und der Anzahl von Neuronen der Zwischenschicht groß genug ist, um zu verhindern, daß das Netzwerk (30) von Neuronen (34) sich auf die Beispiele spezialisiert.
  9. Vorrichtung nach Anspruch 8, dadurch gekennzeichnet, daß die Anzahl von Beispielen in der Größenordnung von zwanzig liegt und daß die Zwischenschicht sechs Neuronen enthält.
  10. Vorrichtung nach einem der vorstehenden Ansprüche, dadurch gekennzeichnet, daß der Ofen ein herkömmlicher Ofen ist.
EP19990400041 1998-01-08 1999-01-08 Automatisches Kochgerät mit einem neuronalen Netzwerk Expired - Lifetime EP0928929B1 (de)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FR9800135 1998-01-08
FR9800135A FR2773390B1 (fr) 1998-01-08 1998-01-08 Dispositif de cuisson automatique utilisant un reseau de neurones

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EP0928929A1 EP0928929A1 (de) 1999-07-14
EP0928929B1 true EP0928929B1 (de) 2003-04-16

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EP (1) EP0928929B1 (de)
DE (1) DE69906826T2 (de)
ES (1) ES2195522T3 (de)
FR (1) FR2773390B1 (de)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10336114A1 (de) 2003-08-06 2005-02-24 BSH Bosch und Siemens Hausgeräte GmbH Gargerät mit einer Bräunungssensorvorrichtung
ITPN20050020A1 (it) 2005-04-05 2006-10-06 Electrolux Professional Spa "congelatore perfezionato con rete neutrale"
WO2009027304A1 (en) 2007-08-24 2009-03-05 Arcelik Anonim Sirketi An oven
CN105444222B (zh) * 2015-12-11 2017-11-14 美的集团股份有限公司 微波炉的烹饪控制方法、***、云服务器和微波炉
CN107965803A (zh) * 2017-11-16 2018-04-27 广东永衡良品科技有限公司 一种提醒防干烧的智能检测装置及其控制方法

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2077018C (en) * 1991-08-30 1997-04-15 Kazunari Nishii Cooking appliance
JPH05172334A (ja) * 1991-10-21 1993-07-09 Matsushita Electric Ind Co Ltd 調理器具
JP2936853B2 (ja) * 1991-12-20 1999-08-23 松下電器産業株式会社 調理器具
US5893051A (en) * 1994-09-27 1999-04-06 Matsushita Electric Industrial Co., Ltd. Method of estimating temperature inside material to be cooked and cooking apparatus for effecting same

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Publication number Publication date
EP0928929A1 (de) 1999-07-14
DE69906826D1 (de) 2003-05-22
ES2195522T3 (es) 2003-12-01
DE69906826T2 (de) 2004-03-04
FR2773390B1 (fr) 2000-03-24
FR2773390A1 (fr) 1999-07-09

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